This script analyze 10x scRNA-seq data from E15, E16, P1, and P4 wt mice

library(dplyr)
library(Matrix)
require(devtools)
install_version("mvtnorm", version = "1.0-8", repos = "http://cran.us.r-project.org") ##Note that I need version 1.0-8 because the newest version requires R>3.5 (my r version is 3.4). mvtnorm is a dependency for fpc which is a dependency for Seurat.
library(Seurat)
First load E15 data (nGene 1k, nUMI 5k already applied)
load("E15_Oct10_10X_Trachea.RData")
E15_Oct10_mm10.1.2.0_Trachea <- SetAllIdent(object = E15_Oct10_mm10.1.2.0_Trachea, id = "genotype")
second load seu_E16P1P4_wt.RData
load("seu_E16P1P4_wt.RData")
data has been log normalized.
seurat_E15E16P1P4_wt<-MergeSeurat(object1 = seu_E16P1P4_wt,object2 = SubsetData(object=E15_Oct10_mm10.1.2.0_Trachea,ident.use=c("wt")),min.cells = 1,min.genes = 1,project="E15Oct10_E16Dec7_P1Dec11_P4Oct18")
remove old analysis done on the E16P1P4 dataset:
seurat_E15E16P1P4_wt@meta.data<-seurat_E15E16P1P4_wt@meta.data[,-which(names(seurat_E15E16P1P4_wt@meta.data) %in% c("res.0.8", "res.1.2","res.1.4","cell_type","S.Score","G2M.Score","Phase","old.ident","CellCycle_score","mocosaGoblet_score","ciliopathy_score","PCD_score"))]
Basic stats about the data:
median(seurat_E15E16P1P4_wt@meta.data$nGene) 
[1] 2979
mean(seurat_E15E16P1P4_wt@meta.data$nGene) 
[1] 3220.857
median(seurat_E15E16P1P4_wt@meta.data$nUMI) 
[1] 9872
mean(seurat_E15E16P1P4_wt@meta.data$nUMI) 
[1] 12465.92
table(seurat_E15E16P1P4_wt@meta.data$age) 

 E15  E16   P1   P4 
4398 8432 2270 1709 
table(seurat_E15E16P1P4_wt@meta.data$seq_group) 

E15_Oct10_wt_green   E15_Oct10_wt_red      E16_Dec7_wt_1      E16_Dec7_wt_6      P1_Dec11_wt_6      P1_Dec11_wt_7 
              2806               1592               3338               5094               1110               1160 
 P4_Oct18_wt_green    P4_Oct18_wt_red 
               803                906 
scale the data:
seurat_E15E16P1P4_wt <- ScaleData(object = seurat_E15E16P1P4_wt)
find variable genes and run PCA:
seurat_E15E16P1P4_wt <- FindVariableGenes(object = seurat_E15E16P1P4_wt, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
seurat_E15E16P1P4_wt <- RunPCA(object = seurat_E15E16P1P4_wt,pcs.compute = 25, do.print = FALSE)
seurat_E15E16P1P4_wt <- ProjectPCA(object = seurat_E15E16P1P4_wt, do.print = FALSE)

PCElbowPlot(object = seurat_E15E16P1P4_wt,num.pc = 25)
clustering:
try resolution=0.8:
n.pcs = 24
res.used <- 0.8

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

resolution=1.4:
n.pcs = 24
res.used <- 1.4
seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
k.param=15 to have better distinction for rare cells:
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.4")

res1.6 leads to further specification of immune cells, P1 and P4 mesenchymal progenitor separation, and separation of E16 basal:
n.pcs = 24
res.used <- 1.6

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.6")
We will use resolution of 1.4 for further analysis.

——————————————————————–

#save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData")  # this seurat object will be updated and saved as analysis goes
#load("seuratE15E16P1P4_wt.RData")

2 E16 wt mice:

2 P1 wt mice:

Annotate clusters by known markers:
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Krt4","Scgb3a2","Spdef","Creb3l1","Muc5b","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Fcer1g","Pecam1","Acta2","Myh11","Col11a1","Acan","Wnt2","Pi16","Ly6a","Twist2","Mki67","Top2a","Tg","Pax8"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 25,cex.row=30,group.order = c(3,8,11,9,1,30,2,20,29,7,26,27,28,14,31,23,18,21,13,15,19,16,10,12,6,4,17,0,5,33,22,25,24,32)
  )

cluster annotation v1:
library(plyr)
seurat_E15E16P1P4_wt@meta.data$cell_type<-mapvalues(seurat_E15E16P1P4_wt@meta.data$res.1.4,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33"),to=c("Fibroblast","Basal","Secretory","Basal","Fibroblast","Fibroblast","Fibroblast","Secretory","CyclingEpithelial","Transitional","Fibroblast","CyclingEpithelial","Fibroblast","Fibroblast","Immune","Fibroblast","Fibroblast","Fibroblast","Muscle","Fibroblast","Ciliated","Muscle","MesenchymalProgenitor","Endothelial","Thyroid","MesenchymalProgenitor","Neural/Schwann","Ciliated","CiliaSecretory","Basal","CyclingEpithelial","Endothelial","Doublet","MesenchymalProgenitor"))
use nonShh subset clustering information (seurat object: E15E16P1P4_wt_nonShh resolution=1.6) to annotate those non-epithelial cells:
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_nonShh_cellType1.6, col.name = "specific_type")
table(seurat_E15E16P1P4_wt@meta.data$specific_type)

          Chondrocyte     CyclingFibroblast            Fibroblast              Immune_1              Immune_2 
                  956                  1516                  4688                   347                   166 
 LymphaticEndothelial MesenchymalProgenitor                Muscle            Neuron/NEC                   RBC 
                  134                   511                   720                    51                    59 
          SchwannCell   VascularEndothelial         VSMC/pericyte 
                   94                   268                    80 
keep the epithelial cell annotation as in seurat_E15E16P1P4_wt@meta.data$cell_type:
seurat_E15E16P1P4_wt@meta.data$specific_type <- ifelse(is.na(seurat_E15E16P1P4_wt@meta.data$specific_type), as.character(seurat_E15E16P1P4_wt@meta.data$cell_type), as.character(seurat_E15E16P1P4_wt@meta.data$specific_type))
now we have new (more detailed) annotation for all cells:
table(seurat_E15E16P1P4_wt@meta.data$specific_type)

                Basal           Chondrocyte        CiliaSecretory              Ciliated     CyclingEpithelial 
                 2491                   956                   196                   571                  1297 
    CyclingFibroblast               Doublet            Fibroblast              Immune_1              Immune_2 
                 1516                    89                  4688                   347                   166 
 LymphaticEndothelial MesenchymalProgenitor                Muscle            Neuron/NEC                   RBC 
                  134                   511                   720                    51                    59 
          SchwannCell             Secretory               Thyroid          Transitional   VascularEndothelial 
                   94                  1729                   245                   601                   268 
        VSMC/pericyte 
                   80 
table(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$gate,useNA = "always")
                       
                        green  red <NA>
  Basal                  1106    1 1384
  Chondrocyte               4  327  625
  CiliaSecretory           76    0  120
  Ciliated                202    2  367
  CyclingEpithelial      1160    3  134
  CyclingFibroblast         4  374 1138
  Doublet                   0    0   89
  Fibroblast               27  833 3828
  Immune_1                  0   72  275
  Immune_2                  0   46  120
  LymphaticEndothelial      0   33  101
  MesenchymalProgenitor     7  192  312
  Muscle                    1  251  468
  Neuron/NEC                0   36   15
  RBC                       0    1   58
  SchwannCell               0   36   58
  Secretory               412    1 1316
  Thyroid                   8  213   24
  Transitional            601    0    0
  VascularEndothelial       0   67  201
  VSMC/pericyte             1   10   69
  <NA>                      0    0    0
specific_type of each cell is defined later.

find differentially expressed genes between clusters of interest:
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
E15E16P1P4_wt_res14_BasalSecretory<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(9,30),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 44s      
   |++                                                | 2 % ~02m 46s      
   |++                                                | 3 % ~02m 41s      
   |+++                                               | 4 % ~02m 38s      
   |+++                                               | 6 % ~02m 34s      
   |++++                                              | 7 % ~02m 32s      
   |++++                                              | 8 % ~02m 33s      
   |+++++                                             | 9 % ~02m 31s      
   |++++++                                            | 10% ~02m 29s      
   |++++++                                            | 11% ~02m 26s      
   |+++++++                                           | 12% ~02m 24s      
   |+++++++                                           | 13% ~02m 22s      
   |++++++++                                          | 15% ~02m 20s      
   |++++++++                                          | 16% ~02m 22s      
   |+++++++++                                         | 17% ~02m 20s      
   |+++++++++                                         | 18% ~02m 17s      
   |++++++++++                                        | 19% ~02m 15s      
   |+++++++++++                                       | 20% ~02m 13s      
   |+++++++++++                                       | 21% ~02m 10s      
   |++++++++++++                                      | 22% ~02m 08s      
   |++++++++++++                                      | 24% ~02m 06s      
   |+++++++++++++                                     | 25% ~02m 04s      
   |+++++++++++++                                     | 26% ~02m 02s      
   |++++++++++++++                                    | 27% ~02m 00s      
   |+++++++++++++++                                   | 28% ~01m 58s      
   |+++++++++++++++                                   | 29% ~01m 56s      
   |++++++++++++++++                                  | 30% ~01m 54s      
   |++++++++++++++++                                  | 31% ~01m 53s      
   |+++++++++++++++++                                 | 33% ~01m 51s      
   |+++++++++++++++++                                 | 34% ~01m 49s      
   |++++++++++++++++++                                | 35% ~01m 47s      
   |++++++++++++++++++                                | 36% ~01m 45s      
   |+++++++++++++++++++                               | 37% ~01m 44s      
   |++++++++++++++++++++                              | 38% ~01m 42s      
   |++++++++++++++++++++                              | 39% ~01m 40s      
   |+++++++++++++++++++++                             | 40% ~01m 39s      
   |+++++++++++++++++++++                             | 42% ~01m 37s      
   |++++++++++++++++++++++                            | 43% ~01m 35s      
   |++++++++++++++++++++++                            | 44% ~01m 33s      
   |+++++++++++++++++++++++                           | 45% ~01m 31s      
   |++++++++++++++++++++++++                          | 46% ~01m 29s      
   |++++++++++++++++++++++++                          | 47% ~01m 27s      
   |+++++++++++++++++++++++++                         | 48% ~01m 25s      
   |+++++++++++++++++++++++++                         | 49% ~01m 23s      
   |++++++++++++++++++++++++++                        | 51% ~01m 21s      
   |++++++++++++++++++++++++++                        | 52% ~01m 19s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 17s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 16s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 14s      
   |+++++++++++++++++++++++++++++                     | 56% ~01m 12s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 10s      
   |++++++++++++++++++++++++++++++                    | 58% ~01m 08s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 06s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 05s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 03s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 01s      
   |+++++++++++++++++++++++++++++++++                 | 64% ~59s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~57s          
   |++++++++++++++++++++++++++++++++++                | 66% ~56s          
   |++++++++++++++++++++++++++++++++++                | 67% ~54s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~52s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~50s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~49s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~47s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~45s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~43s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~41s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~39s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~37s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~35s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~33s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~32s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~30s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~28s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~26s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~24s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~15s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~13s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~11s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 46s
E15E16P1P4_wt_res14_BasalSecretory
For the purpose of visualization, we average within each specific cell type:
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
average_wt_specific_Annotation<-AverageExpression(object = seurat_E15E16P1P4_wt,return.seurat = T)
Finished averaging RNA for cluster Basal
Finished averaging RNA for cluster Chondrocyte
Finished averaging RNA for cluster CiliaSecretory
Finished averaging RNA for cluster Ciliated
Finished averaging RNA for cluster CyclingEpithelial
Finished averaging RNA for cluster CyclingFibroblast
Finished averaging RNA for cluster Doublet
Finished averaging RNA for cluster Fibroblast
Finished averaging RNA for cluster Immune_1
Finished averaging RNA for cluster Immune_2
Finished averaging RNA for cluster LymphaticEndothelial
Finished averaging RNA for cluster MesenchymalProgenitor
Finished averaging RNA for cluster Muscle
Finished averaging RNA for cluster Neuron/NEC
Finished averaging RNA for cluster RBC
Finished averaging RNA for cluster SchwannCell
Finished averaging RNA for cluster Secretory
Finished averaging RNA for cluster Thyroid
Finished averaging RNA for cluster Transitional
Finished averaging RNA for cluster VascularEndothelial
Finished averaging RNA for cluster VSMC/pericyte
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Scaling data matrix

  |                                                                                                                    
  |                                                                                                              |   0%
  |                                                                                                                    
  |==============================================================================================================| 100%

print(levels(seurat_E15E16P1P4_wt@ident))

epithlial cells across ages:

df_wt<-FetchData(seurat_E15E16P1P4_wt,c("Ano1","Cftr","Krt4","Krt13","res.1.4","age","seq_group","cell_type","specific_type"))

any possible ionocytes?:

table(df_all_wt$res.1.4[df_all_wt$Foxi1>0])

 0  1 10 11 12 13 14 15 16 17 18 19  2 20 21 22 23 24 25 26 27 28 29  3 30 31 32 33  4  5  6  7  8  9 
 0  0  0  0  0  0  2  0  1  1  0  0  2  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 
table(df_all_wt$res.1.4[df_all_wt$Cftr>0 & df_all_wt$Foxi1>0])

 0  1 10 11 12 13 14 15 16 17 18 19  2 20 21 22 23 24 25 26 27 28 29  3 30 31 32 33  4  5  6  7  8  9 
 0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 
epithelial cell composition across ages:

table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)],seurat_E15E16P1P4_wt@meta.data$cell_type[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)])
     
      Basal CiliaSecretory Ciliated CyclingEpithelial Secretory Transitional
  E15  1017              0        1              1155         0          601
  E16  1324              3      294               132      1094            0
  P1     60            117       73                 2       222            0
  P4     90             76      203                 8       413            0
a few lineage markers:

seurat_E15E16P1P4_wt=buildClusterTree(seurat_E15E16P1P4_wt,do.reorder = F,reorder.numeric = F,pcs.use = 1:24)
'buildClusterTree' is deprecated.
Use 'BuildClusterTree' instead.
See help("Deprecated") and help("Seurat-deprecated").Finished averaging RNA for cluster 0
Finished averaging RNA for cluster 1
Finished averaging RNA for cluster 2
Finished averaging RNA for cluster 3
Finished averaging RNA for cluster 4
Finished averaging RNA for cluster 5
Finished averaging RNA for cluster 6
Finished averaging RNA for cluster 7
Finished averaging RNA for cluster 8
Finished averaging RNA for cluster 9
Finished averaging RNA for cluster 10
Finished averaging RNA for cluster 11
Finished averaging RNA for cluster 12
Finished averaging RNA for cluster 13
Finished averaging RNA for cluster 14
Finished averaging RNA for cluster 15
Finished averaging RNA for cluster 16
Finished averaging RNA for cluster 17
Finished averaging RNA for cluster 18
Finished averaging RNA for cluster 19
Finished averaging RNA for cluster 20
Finished averaging RNA for cluster 21
Finished averaging RNA for cluster 22
Finished averaging RNA for cluster 23
Finished averaging RNA for cluster 24
Finished averaging RNA for cluster 25
Finished averaging RNA for cluster 26
Finished averaging RNA for cluster 27
Finished averaging RNA for cluster 28
Finished averaging RNA for cluster 29
Finished averaging RNA for cluster 30
Finished averaging RNA for cluster 31
Finished averaging RNA for cluster 32
Finished averaging RNA for cluster 33

potential mesenchymal progenitors:
E15E16P1P4_wt_res14_22over33<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(22),ident.2 = c(33),only.pos = TRUE)
E15E16P1P4_wt_res14_22over33
E15E16P1P4_wt_res14_33over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(33),ident.2 = c(22),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~07s          
   |++                                                | 2 % ~07s          
   |++                                                | 3 % ~07s          
   |+++                                               | 4 % ~07s          
   |+++                                               | 5 % ~13s          
   |++++                                              | 6 % ~12s          
   |++++                                              | 8 % ~11s          
   |+++++                                             | 9 % ~10s          
   |+++++                                             | 10% ~10s          
   |++++++                                            | 11% ~09s          
   |++++++                                            | 12% ~09s          
   |+++++++                                           | 13% ~09s          
   |+++++++                                           | 14% ~08s          
   |++++++++                                          | 15% ~08s          
   |+++++++++                                         | 16% ~08s          
   |+++++++++                                         | 17% ~08s          
   |++++++++++                                        | 18% ~07s          
   |++++++++++                                        | 19% ~07s          
   |+++++++++++                                       | 20% ~07s          
   |+++++++++++                                       | 22% ~07s          
   |++++++++++++                                      | 23% ~07s          
   |++++++++++++                                      | 24% ~07s          
   |+++++++++++++                                     | 25% ~06s          
   |+++++++++++++                                     | 26% ~06s          
   |++++++++++++++                                    | 27% ~06s          
   |++++++++++++++                                    | 28% ~06s          
   |+++++++++++++++                                   | 29% ~06s          
   |++++++++++++++++                                  | 30% ~06s          
   |++++++++++++++++                                  | 31% ~06s          
   |+++++++++++++++++                                 | 32% ~06s          
   |+++++++++++++++++                                 | 33% ~06s          
   |++++++++++++++++++                                | 34% ~05s          
   |++++++++++++++++++                                | 35% ~05s          
   |+++++++++++++++++++                               | 37% ~05s          
   |+++++++++++++++++++                               | 38% ~05s          
   |++++++++++++++++++++                              | 39% ~05s          
   |++++++++++++++++++++                              | 40% ~05s          
   |+++++++++++++++++++++                             | 41% ~05s          
   |+++++++++++++++++++++                             | 42% ~05s          
   |++++++++++++++++++++++                            | 43% ~05s          
   |+++++++++++++++++++++++                           | 44% ~04s          
   |+++++++++++++++++++++++                           | 45% ~04s          
   |++++++++++++++++++++++++                          | 46% ~04s          
   |++++++++++++++++++++++++                          | 47% ~04s          
   |+++++++++++++++++++++++++                         | 48% ~04s          
   |+++++++++++++++++++++++++                         | 49% ~04s          
   |++++++++++++++++++++++++++                        | 51% ~04s          
   |++++++++++++++++++++++++++                        | 52% ~04s          
   |+++++++++++++++++++++++++++                       | 53% ~04s          
   |+++++++++++++++++++++++++++                       | 54% ~04s          
   |++++++++++++++++++++++++++++                      | 55% ~04s          
   |++++++++++++++++++++++++++++                      | 56% ~03s          
   |+++++++++++++++++++++++++++++                     | 57% ~03s          
   |++++++++++++++++++++++++++++++                    | 58% ~03s          
   |++++++++++++++++++++++++++++++                    | 59% ~03s          
   |+++++++++++++++++++++++++++++++                   | 60% ~03s          
   |+++++++++++++++++++++++++++++++                   | 61% ~03s          
   |++++++++++++++++++++++++++++++++                  | 62% ~03s          
   |++++++++++++++++++++++++++++++++                  | 63% ~03s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
   |++++++++++++++++++++++++++++++++++                | 67% ~03s          
   |++++++++++++++++++++++++++++++++++                | 68% ~02s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~02s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~02s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~02s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 08s
E15E16P1P4_wt_res14_33over22
E15E16P1P4_wt_res14_25over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(25),ident.2 = c(22),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~05s          
   |++                                                | 2 % ~04s          
   |++                                                | 4 % ~04s          
   |+++                                               | 5 % ~04s          
   |+++                                               | 6 % ~04s          
   |++++                                              | 7 % ~04s          
   |+++++                                             | 8 % ~04s          
   |+++++                                             | 9 % ~04s          
   |++++++                                            | 11% ~04s          
   |++++++                                            | 12% ~04s          
   |+++++++                                           | 13% ~04s          
   |++++++++                                          | 14% ~04s          
   |++++++++                                          | 15% ~04s          
   |+++++++++                                         | 16% ~04s          
   |+++++++++                                         | 18% ~04s          
   |++++++++++                                        | 19% ~04s          
   |++++++++++                                        | 20% ~04s          
   |+++++++++++                                       | 21% ~04s          
   |++++++++++++                                      | 22% ~03s          
   |++++++++++++                                      | 24% ~03s          
   |+++++++++++++                                     | 25% ~03s          
   |+++++++++++++                                     | 26% ~03s          
   |++++++++++++++                                    | 27% ~03s          
   |+++++++++++++++                                   | 28% ~03s          
   |+++++++++++++++                                   | 29% ~03s          
   |++++++++++++++++                                  | 31% ~03s          
   |++++++++++++++++                                  | 32% ~03s          
   |+++++++++++++++++                                 | 33% ~03s          
   |++++++++++++++++++                                | 34% ~03s          
   |++++++++++++++++++                                | 35% ~03s          
   |+++++++++++++++++++                               | 36% ~03s          
   |+++++++++++++++++++                               | 38% ~03s          
   |++++++++++++++++++++                              | 39% ~03s          
   |++++++++++++++++++++                              | 40% ~03s          
   |+++++++++++++++++++++                             | 41% ~03s          
   |++++++++++++++++++++++                            | 42% ~03s          
   |++++++++++++++++++++++                            | 44% ~03s          
   |+++++++++++++++++++++++                           | 45% ~03s          
   |+++++++++++++++++++++++                           | 46% ~02s          
   |++++++++++++++++++++++++                          | 47% ~02s          
   |+++++++++++++++++++++++++                         | 48% ~02s          
   |+++++++++++++++++++++++++                         | 49% ~02s          
   |++++++++++++++++++++++++++                        | 51% ~02s          
   |++++++++++++++++++++++++++                        | 52% ~02s          
   |+++++++++++++++++++++++++++                       | 53% ~02s          
   |++++++++++++++++++++++++++++                      | 54% ~02s          
   |++++++++++++++++++++++++++++                      | 55% ~02s          
   |+++++++++++++++++++++++++++++                     | 56% ~02s          
   |+++++++++++++++++++++++++++++                     | 58% ~02s          
   |++++++++++++++++++++++++++++++                    | 59% ~02s          
   |++++++++++++++++++++++++++++++                    | 60% ~02s          
   |+++++++++++++++++++++++++++++++                   | 61% ~02s          
   |++++++++++++++++++++++++++++++++                  | 62% ~02s          
   |++++++++++++++++++++++++++++++++                  | 64% ~02s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~02s          
   |++++++++++++++++++++++++++++++++++                | 67% ~01s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05s
E15E16P1P4_wt_res14_25over22

scGPS (scoring):
head(geneList$Mucociliary)
[1] "Fcgr3"  "Dnah7c" "Dnah7b" "Dnah7a" "Mst1"   "Mst1r" 
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:plyr’:

    arrange, count, desc, failwith, id, mutate, rename, summarise, summarize

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
percentile_table_E15E16P1P4wt<-apply(seurat_E15E16P1P4_wt@data,1,percent_rank)
percentile_table_E15E16P1P4wt[1:6,1:6]
                               Xkr4 Gm1992 Gm37381       Rp1 Sox17 Gm37323
E16_Dec7_wt_1_AAACCTGAGATCCCGC    0      0       0 0.0000000     0       0
E16_Dec7_wt_1_AAACCTGAGGTGCAAC    0      0       0 0.0000000     0       0
E16_Dec7_wt_1_AAACCTGGTAAATGAC    0      0       0 0.0000000     0       0
E16_Dec7_wt_1_AAACCTGGTGATGCCC    0      0       0 0.0000000     0       0
E16_Dec7_wt_1_AAACCTGTCACATACG    0      0       0 0.0000000     0       0
E16_Dec7_wt_1_AAACCTGTCAGCGACC    0      0       0 0.9869705     0       0
 CellCycle_score_wt<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Cell_cycle],1,mean)
 head( CellCycle_score_wt)
E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC 
                    0.04833364                     0.10794933                     0.06216909 
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC 
                    0.66890828                     0.10226353                     0.36390747 
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = CellCycle_score_wt, col.name = "CellCycle_score")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("CellCycle_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

wt_mocosaGoblet_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],1,mean)
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_mocosaGoblet_score, col.name = "mucosaGoblet_score")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

 wt_ciliopathy_table<- percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Ciliopathy]
 wt_ciliopathy_score<- apply(wt_ciliopathy_table,1,mean)
 head(wt_ciliopathy_score)
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_ciliopathy_score, col.name = "ciliopathy_score")

 wt_PCD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Primary.ciliary.dyskinesia],1,mean)
 head(wt_PCD_score)
E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC 
                    0.07178531                     0.06811267                     0.05672251 
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC 
                    0.09034630                     0.03588221                     0.66970120 
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_PCD_score, col.name = "PCD_score")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Secretory","CiliaSecretory"),],aes(age,mucosaGoblet_score))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.01,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))

 wt_bronchitis_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Bronchiectasis.and.Bronchitis],1,mean)
 head(wt_bronchitis_score)
E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC E16_Dec7_wt_1_AAACCTGGTGATGCCC 
                    0.05980333                     0.12838142                     0.10012107                     0.07827476 
E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC 
                    0.09195436                     0.36792222 
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_bronchitis_score, col.name = "Bronchiectasis_Bronchitis")

seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Bronchiectasis_Bronchitis"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

 wt_COPD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$COPD],1,mean)
 head(wt_COPD_score)
E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC 
                     0.1345453                      0.1974387                      0.1539624 
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC 
                     0.1719776                      0.1320101                      0.2590983 
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_COPD_score, col.name = "COPD")

 wt_asthma_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Pulmonary...Asthma],1,mean)
 head(wt_asthma_score)
E16_Dec7_wt_1_AAACCTGAGATCCCGC E16_Dec7_wt_1_AAACCTGAGGTGCAAC E16_Dec7_wt_1_AAACCTGGTAAATGAC 
                    0.08485814                     0.13134618                     0.09996662 
E16_Dec7_wt_1_AAACCTGGTGATGCCC E16_Dec7_wt_1_AAACCTGTCACATACG E16_Dec7_wt_1_AAACCTGTCAGCGACC 
                    0.09764181                     0.08522185                     0.14710912 
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_asthma_score, col.name = "Asthma")

check some human GWAS:
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Apip","Hhip","Chrna5","Htr4","Adgrg6","Thsd4","Fam13a","Gstcd","Rin3","Adam19","Tet2","Eefsec","Cfdp1","Tgfb2","Ager","Sgf29","Armc2","Pid1","Dsp","Mtcl1","Rarb","Sftpd","Cyp2a4","Cyp2a5"),group.by = "ident", x.lab.rot = T,plot.legend = T)

CF GWAS:
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Aplp1","Aplp2","H2-Aa","H2-Ab1","H2-Eb1","H2-Eb2"),group.by = "ident", x.lab.rot = T,plot.legend = T)

COPD:

Developmental lanscape:
basal cells across time:
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
E15E16P1P4_wt_res14_3over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(3),ident.2 = c(1),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~30s          
   |++                                                | 2 % ~28s          
   |++                                                | 3 % ~27s          
   |+++                                               | 4 % ~26s          
   |+++                                               | 5 % ~25s          
   |++++                                              | 6 % ~25s          
   |++++                                              | 7 % ~24s          
   |+++++                                             | 8 % ~24s          
   |+++++                                             | 9 % ~24s          
   |++++++                                            | 10% ~23s          
   |++++++                                            | 11% ~28s          
   |+++++++                                           | 12% ~27s          
   |+++++++                                           | 13% ~27s          
   |++++++++                                          | 14% ~26s          
   |++++++++                                          | 15% ~26s          
   |+++++++++                                         | 16% ~25s          
   |+++++++++                                         | 17% ~25s          
   |++++++++++                                        | 18% ~24s          
   |++++++++++                                        | 19% ~24s          
   |+++++++++++                                       | 20% ~23s          
   |+++++++++++                                       | 21% ~23s          
   |++++++++++++                                      | 22% ~22s          
   |++++++++++++                                      | 23% ~22s          
   |+++++++++++++                                     | 24% ~22s          
   |+++++++++++++                                     | 26% ~21s          
   |++++++++++++++                                    | 27% ~21s          
   |++++++++++++++                                    | 28% ~21s          
   |+++++++++++++++                                   | 29% ~20s          
   |+++++++++++++++                                   | 30% ~20s          
   |++++++++++++++++                                  | 31% ~20s          
   |++++++++++++++++                                  | 32% ~19s          
   |+++++++++++++++++                                 | 33% ~19s          
   |+++++++++++++++++                                 | 34% ~19s          
   |++++++++++++++++++                                | 35% ~18s          
   |++++++++++++++++++                                | 36% ~18s          
   |+++++++++++++++++++                               | 37% ~18s          
   |+++++++++++++++++++                               | 38% ~17s          
   |++++++++++++++++++++                              | 39% ~17s          
   |++++++++++++++++++++                              | 40% ~17s          
   |+++++++++++++++++++++                             | 41% ~16s          
   |+++++++++++++++++++++                             | 42% ~16s          
   |++++++++++++++++++++++                            | 43% ~16s          
   |++++++++++++++++++++++                            | 44% ~15s          
   |+++++++++++++++++++++++                           | 45% ~15s          
   |+++++++++++++++++++++++                           | 46% ~15s          
   |++++++++++++++++++++++++                          | 47% ~15s          
   |++++++++++++++++++++++++                          | 48% ~14s          
   |+++++++++++++++++++++++++                         | 49% ~14s          
   |+++++++++++++++++++++++++                         | 50% ~14s          
   |++++++++++++++++++++++++++                        | 51% ~13s          
   |+++++++++++++++++++++++++++                       | 52% ~13s          
   |+++++++++++++++++++++++++++                       | 53% ~13s          
   |++++++++++++++++++++++++++++                      | 54% ~13s          
   |++++++++++++++++++++++++++++                      | 55% ~12s          
   |+++++++++++++++++++++++++++++                     | 56% ~12s          
   |+++++++++++++++++++++++++++++                     | 57% ~12s          
   |++++++++++++++++++++++++++++++                    | 58% ~11s          
   |++++++++++++++++++++++++++++++                    | 59% ~11s          
   |+++++++++++++++++++++++++++++++                   | 60% ~11s          
   |+++++++++++++++++++++++++++++++                   | 61% ~11s          
   |++++++++++++++++++++++++++++++++                  | 62% ~10s          
   |++++++++++++++++++++++++++++++++                  | 63% ~10s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~10s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~09s          
   |++++++++++++++++++++++++++++++++++                | 66% ~09s          
   |++++++++++++++++++++++++++++++++++                | 67% ~09s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~09s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~08s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~08s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~08s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~07s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~07s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~07s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~07s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~06s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~06s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~06s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 27s
E15E16P1P4_wt_res14_3over1
write.table(E15E16P1P4_wt_res14_3over1,"E15E16P1P4_wt_res14_3over1.txt",sep="\t")
E15E16P1P4_wt_res14_1over3<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(1),ident.2 = c(3),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~15s          
   |++                                                | 2 % ~14s          
   |++                                                | 3 % ~14s          
   |+++                                               | 5 % ~14s          
   |+++                                               | 6 % ~14s          
   |++++                                              | 7 % ~14s          
   |++++                                              | 8 % ~14s          
   |+++++                                             | 9 % ~14s          
   |++++++                                            | 10% ~13s          
   |++++++                                            | 11% ~13s          
   |+++++++                                           | 12% ~13s          
   |+++++++                                           | 14% ~13s          
   |++++++++                                          | 15% ~13s          
   |++++++++                                          | 16% ~12s          
   |+++++++++                                         | 17% ~12s          
   |++++++++++                                        | 18% ~12s          
   |++++++++++                                        | 19% ~12s          
   |+++++++++++                                       | 20% ~12s          
   |+++++++++++                                       | 22% ~12s          
   |++++++++++++                                      | 23% ~11s          
   |++++++++++++                                      | 24% ~11s          
   |+++++++++++++                                     | 25% ~11s          
   |++++++++++++++                                    | 26% ~11s          
   |++++++++++++++                                    | 27% ~11s          
   |+++++++++++++++                                   | 28% ~11s          
   |+++++++++++++++                                   | 30% ~10s          
   |++++++++++++++++                                  | 31% ~10s          
   |++++++++++++++++                                  | 32% ~10s          
   |+++++++++++++++++                                 | 33% ~10s          
   |++++++++++++++++++                                | 34% ~10s          
   |++++++++++++++++++                                | 35% ~09s          
   |+++++++++++++++++++                               | 36% ~09s          
   |+++++++++++++++++++                               | 38% ~09s          
   |++++++++++++++++++++                              | 39% ~09s          
   |++++++++++++++++++++                              | 40% ~09s          
   |+++++++++++++++++++++                             | 41% ~09s          
   |++++++++++++++++++++++                            | 42% ~09s          
   |++++++++++++++++++++++                            | 43% ~08s          
   |+++++++++++++++++++++++                           | 44% ~08s          
   |+++++++++++++++++++++++                           | 45% ~08s          
   |++++++++++++++++++++++++                          | 47% ~08s          
   |++++++++++++++++++++++++                          | 48% ~08s          
   |+++++++++++++++++++++++++                         | 49% ~08s          
   |+++++++++++++++++++++++++                         | 50% ~07s          
   |++++++++++++++++++++++++++                        | 51% ~07s          
   |+++++++++++++++++++++++++++                       | 52% ~07s          
   |+++++++++++++++++++++++++++                       | 53% ~07s          
   |++++++++++++++++++++++++++++                      | 55% ~07s          
   |++++++++++++++++++++++++++++                      | 56% ~07s          
   |+++++++++++++++++++++++++++++                     | 57% ~06s          
   |+++++++++++++++++++++++++++++                     | 58% ~06s          
   |++++++++++++++++++++++++++++++                    | 59% ~06s          
   |+++++++++++++++++++++++++++++++                   | 60% ~06s          
   |+++++++++++++++++++++++++++++++                   | 61% ~06s          
   |++++++++++++++++++++++++++++++++                  | 62% ~06s          
   |++++++++++++++++++++++++++++++++                  | 64% ~05s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~05s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~05s          
   |++++++++++++++++++++++++++++++++++                | 67% ~05s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~05s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~04s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~04s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~04s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~04s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~03s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 15s
E15E16P1P4_wt_res14_1over3
write.table(E15E16P1P4_wt_res14_1over3,"E15E16P1P4_wt_res14_1over3.txt",sep="\t")
E15E16P1P4_wt_res14_29over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(29),ident.2 = c(1),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~10s          
   |++                                                | 2 % ~09s          
   |++                                                | 3 % ~09s          
   |+++                                               | 4 % ~09s          
   |+++                                               | 5 % ~09s          
   |++++                                              | 6 % ~09s          
   |++++                                              | 8 % ~09s          
   |+++++                                             | 9 % ~09s          
   |+++++                                             | 10% ~09s          
   |++++++                                            | 11% ~09s          
   |++++++                                            | 12% ~08s          
   |+++++++                                           | 13% ~08s          
   |+++++++                                           | 14% ~08s          
   |++++++++                                          | 15% ~08s          
   |+++++++++                                         | 16% ~08s          
   |+++++++++                                         | 17% ~08s          
   |++++++++++                                        | 18% ~08s          
   |++++++++++                                        | 19% ~08s          
   |+++++++++++                                       | 20% ~08s          
   |+++++++++++                                       | 22% ~08s          
   |++++++++++++                                      | 23% ~07s          
   |++++++++++++                                      | 24% ~07s          
   |+++++++++++++                                     | 25% ~07s          
   |+++++++++++++                                     | 26% ~07s          
   |++++++++++++++                                    | 27% ~07s          
   |++++++++++++++                                    | 28% ~07s          
   |+++++++++++++++                                   | 29% ~07s          
   |++++++++++++++++                                  | 30% ~07s          
   |++++++++++++++++                                  | 31% ~07s          
   |+++++++++++++++++                                 | 32% ~06s          
   |+++++++++++++++++                                 | 33% ~06s          
   |++++++++++++++++++                                | 34% ~06s          
   |++++++++++++++++++                                | 35% ~06s          
   |+++++++++++++++++++                               | 37% ~06s          
   |+++++++++++++++++++                               | 38% ~06s          
   |++++++++++++++++++++                              | 39% ~06s          
   |++++++++++++++++++++                              | 40% ~06s          
   |+++++++++++++++++++++                             | 41% ~06s          
   |+++++++++++++++++++++                             | 42% ~06s          
   |++++++++++++++++++++++                            | 43% ~05s          
   |+++++++++++++++++++++++                           | 44% ~05s          
   |+++++++++++++++++++++++                           | 45% ~05s          
   |++++++++++++++++++++++++                          | 46% ~05s          
   |++++++++++++++++++++++++                          | 47% ~05s          
   |+++++++++++++++++++++++++                         | 48% ~05s          
   |+++++++++++++++++++++++++                         | 49% ~05s          
   |++++++++++++++++++++++++++                        | 51% ~05s          
   |++++++++++++++++++++++++++                        | 52% ~05s          
   |+++++++++++++++++++++++++++                       | 53% ~05s          
   |+++++++++++++++++++++++++++                       | 54% ~04s          
   |++++++++++++++++++++++++++++                      | 55% ~04s          
   |++++++++++++++++++++++++++++                      | 56% ~04s          
   |+++++++++++++++++++++++++++++                     | 57% ~04s          
   |++++++++++++++++++++++++++++++                    | 58% ~04s          
   |++++++++++++++++++++++++++++++                    | 59% ~04s          
   |+++++++++++++++++++++++++++++++                   | 60% ~04s          
   |+++++++++++++++++++++++++++++++                   | 61% ~04s          
   |++++++++++++++++++++++++++++++++                  | 62% ~04s          
   |++++++++++++++++++++++++++++++++                  | 63% ~03s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
   |++++++++++++++++++++++++++++++++++                | 67% ~03s          
   |++++++++++++++++++++++++++++++++++                | 68% ~03s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~03s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~02s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 09s
E15E16P1P4_wt_res14_29over1

df_3_1_29_wt<-FetchData(seurat_E15E16P1P4_wt,c("Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Wnt4","Krt5","Krt15","Aqp3","Aqp4","Aqp5","Rpl6l","Rpl9-ps6","Rpl13-ps3","age","res.1.4"))
df_3_1_29_wt<-df_3_1_29_wt[order(factor(df_3_1_29_wt$res.1.4,levels=c("3","1","29")),df_3_1_29_wt$age),]
age<-substr(rownames(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),]),1,3)
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

   0    1   10   11   12   13   14   15   16   17   18   19    2   20   21   22   23   24   25   26   27 
1347 1324  557  552  531  506  493  474  462  448  395  388 1094  362  342  317  272  245  222  221  209 
  28   29    3   30   31   32   33    4    5    6    7    8    9 
 196  149 1018  137  134   89   88  916  744  733  635  608  601 
library(gplots)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)

library(gplots)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)

pdf(file = paste("Manuscript/heatmap/","basal_dev",".pdf", sep = ""), width = 7, height = 3)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.Error in plot.new() : figure margins too large
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,1,29),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_manual(values = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"))+scale_x_discrete(limits=c("3","1","29"))

ciliated cells across time:
E15E16P1P4_wt_res14_27over20<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(27),ident.2 = c(20),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~12s          
   |++                                                | 2 % ~12s          
   |++                                                | 3 % ~11s          
   |+++                                               | 4 % ~11s          
   |+++                                               | 5 % ~11s          
   |++++                                              | 6 % ~11s          
   |++++                                              | 8 % ~11s          
   |+++++                                             | 9 % ~11s          
   |+++++                                             | 10% ~11s          
   |++++++                                            | 11% ~11s          
   |++++++                                            | 12% ~10s          
   |+++++++                                           | 13% ~10s          
   |+++++++                                           | 14% ~10s          
   |++++++++                                          | 15% ~10s          
   |+++++++++                                         | 16% ~10s          
   |+++++++++                                         | 17% ~10s          
   |++++++++++                                        | 18% ~10s          
   |++++++++++                                        | 19% ~10s          
   |+++++++++++                                       | 20% ~10s          
   |+++++++++++                                       | 22% ~09s          
   |++++++++++++                                      | 23% ~09s          
   |++++++++++++                                      | 24% ~09s          
   |+++++++++++++                                     | 25% ~09s          
   |+++++++++++++                                     | 26% ~09s          
   |++++++++++++++                                    | 27% ~09s          
   |++++++++++++++                                    | 28% ~09s          
   |+++++++++++++++                                   | 29% ~09s          
   |++++++++++++++++                                  | 30% ~09s          
   |++++++++++++++++                                  | 31% ~09s          
   |+++++++++++++++++                                 | 32% ~08s          
   |+++++++++++++++++                                 | 33% ~08s          
   |++++++++++++++++++                                | 34% ~08s          
   |++++++++++++++++++                                | 35% ~08s          
   |+++++++++++++++++++                               | 37% ~08s          
   |+++++++++++++++++++                               | 38% ~08s          
   |++++++++++++++++++++                              | 39% ~08s          
   |++++++++++++++++++++                              | 40% ~07s          
   |+++++++++++++++++++++                             | 41% ~07s          
   |+++++++++++++++++++++                             | 42% ~07s          
   |++++++++++++++++++++++                            | 43% ~07s          
   |+++++++++++++++++++++++                           | 44% ~07s          
   |+++++++++++++++++++++++                           | 45% ~07s          
   |++++++++++++++++++++++++                          | 46% ~07s          
   |++++++++++++++++++++++++                          | 47% ~06s          
   |+++++++++++++++++++++++++                         | 48% ~06s          
   |+++++++++++++++++++++++++                         | 49% ~06s          
   |++++++++++++++++++++++++++                        | 51% ~06s          
   |++++++++++++++++++++++++++                        | 52% ~06s          
   |+++++++++++++++++++++++++++                       | 53% ~06s          
   |+++++++++++++++++++++++++++                       | 54% ~06s          
   |++++++++++++++++++++++++++++                      | 55% ~05s          
   |++++++++++++++++++++++++++++                      | 56% ~05s          
   |+++++++++++++++++++++++++++++                     | 57% ~05s          
   |++++++++++++++++++++++++++++++                    | 58% ~05s          
   |++++++++++++++++++++++++++++++                    | 59% ~05s          
   |+++++++++++++++++++++++++++++++                   | 60% ~05s          
   |+++++++++++++++++++++++++++++++                   | 61% ~05s          
   |++++++++++++++++++++++++++++++++                  | 62% ~05s          
   |++++++++++++++++++++++++++++++++                  | 63% ~04s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~04s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~04s          
   |++++++++++++++++++++++++++++++++++                | 67% ~04s          
   |++++++++++++++++++++++++++++++++++                | 68% ~04s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~04s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~04s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~04s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~03s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~03s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~03s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 12s
E15E16P1P4_wt_res14_27over20
write.table(E15E16P1P4_wt_res14_27over20,"E15E16P1P4_wt_res14_27over20.txt",sep="\t")
E15E16P1P4_wt_res14_20over27<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(20),ident.2 = c(27),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~13s          
   |++                                                | 2 % ~13s          
   |++                                                | 3 % ~11s          
   |+++                                               | 4 % ~10s          
   |+++                                               | 5 % ~10s          
   |++++                                              | 6 % ~10s          
   |++++                                              | 8 % ~10s          
   |+++++                                             | 9 % ~09s          
   |+++++                                             | 10% ~09s          
   |++++++                                            | 11% ~09s          
   |++++++                                            | 12% ~09s          
   |+++++++                                           | 13% ~08s          
   |+++++++                                           | 14% ~09s          
   |++++++++                                          | 15% ~08s          
   |+++++++++                                         | 16% ~08s          
   |+++++++++                                         | 17% ~08s          
   |++++++++++                                        | 18% ~08s          
   |++++++++++                                        | 19% ~08s          
   |+++++++++++                                       | 20% ~08s          
   |+++++++++++                                       | 22% ~08s          
   |++++++++++++                                      | 23% ~07s          
   |++++++++++++                                      | 24% ~07s          
   |+++++++++++++                                     | 25% ~07s          
   |+++++++++++++                                     | 26% ~07s          
   |++++++++++++++                                    | 27% ~07s          
   |++++++++++++++                                    | 28% ~07s          
   |+++++++++++++++                                   | 29% ~07s          
   |++++++++++++++++                                  | 30% ~07s          
   |++++++++++++++++                                  | 31% ~07s          
   |+++++++++++++++++                                 | 32% ~06s          
   |+++++++++++++++++                                 | 33% ~06s          
   |++++++++++++++++++                                | 34% ~06s          
   |++++++++++++++++++                                | 35% ~06s          
   |+++++++++++++++++++                               | 37% ~06s          
   |+++++++++++++++++++                               | 38% ~06s          
   |++++++++++++++++++++                              | 39% ~06s          
   |++++++++++++++++++++                              | 40% ~06s          
   |+++++++++++++++++++++                             | 41% ~06s          
   |+++++++++++++++++++++                             | 42% ~05s          
   |++++++++++++++++++++++                            | 43% ~05s          
   |+++++++++++++++++++++++                           | 44% ~05s          
   |+++++++++++++++++++++++                           | 45% ~05s          
   |++++++++++++++++++++++++                          | 46% ~05s          
   |++++++++++++++++++++++++                          | 47% ~05s          
   |+++++++++++++++++++++++++                         | 48% ~05s          
   |+++++++++++++++++++++++++                         | 49% ~05s          
   |++++++++++++++++++++++++++                        | 51% ~05s          
   |++++++++++++++++++++++++++                        | 52% ~04s          
   |+++++++++++++++++++++++++++                       | 53% ~04s          
   |+++++++++++++++++++++++++++                       | 54% ~04s          
   |++++++++++++++++++++++++++++                      | 55% ~04s          
   |++++++++++++++++++++++++++++                      | 56% ~04s          
   |+++++++++++++++++++++++++++++                     | 57% ~04s          
   |++++++++++++++++++++++++++++++                    | 58% ~04s          
   |++++++++++++++++++++++++++++++                    | 59% ~04s          
   |+++++++++++++++++++++++++++++++                   | 60% ~04s          
   |+++++++++++++++++++++++++++++++                   | 61% ~04s          
   |++++++++++++++++++++++++++++++++                  | 62% ~04s          
   |++++++++++++++++++++++++++++++++                  | 63% ~03s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
   |++++++++++++++++++++++++++++++++++                | 67% ~03s          
   |++++++++++++++++++++++++++++++++++                | 68% ~03s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~03s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~02s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~02s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 09s
E15E16P1P4_wt_res14_20over27
write.table(E15E16P1P4_wt_res14_20over27,"E15E16P1P4_wt_res14_20over27.txt",sep="\t")

table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

   0    1   10   11   12   13   14   15   16   17   18   19    2   20   21   22   23   24   25   26   27 
1347 1324  557  552  531  506  493  474  462  448  395  388 1094  362  342  317  272  245  222  221  209 
  28   29    3   30   31   32   33    4    5    6    7    8    9 
 196  149 1018  137  134   89   88  916  744  733  635  608  601 
df_20_27_wt<-FetchData(seurat_E15E16P1P4_wt,c("Foxj1","Lrrc23","Ccdc67","Cep152","Plk4","Ccna1","Ccno","Cdc20b","Cdc20","Hyls1","Mcidas","Smim24","Ift80","Prr18","Sntn","Stmnd1","Ldlrad1","Cdhr4","Cdhr3","Slc23a2","Ly6a","Ly6c1","Adam8","Cxcl17","Ifitm1","age","res.1.4"))
df_20_27_wt<-df_20_27_wt[order(df_20_27_wt$res.1.4,df_20_27_wt$age),]
age<-substr(rownames(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),]),1,3)
library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:25])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)

library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:22])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)

table(seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)])
prop.table(table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)]),1)
     
              20         27
  E15 1.00000000 0.00000000
  E16 0.98979592 0.01020408
  P1  0.19178082 0.80821918
  P4  0.27586207 0.72413793

secretory cells across time:
E15E16P1P4_wt_res14_2over7<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(2),ident.2 = c(7),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~16s          
   |++                                                | 2 % ~16s          
   |++                                                | 3 % ~16s          
   |+++                                               | 4 % ~15s          
   |+++                                               | 5 % ~15s          
   |++++                                              | 6 % ~15s          
   |++++                                              | 7 % ~15s          
   |+++++                                             | 8 % ~15s          
   |+++++                                             | 9 % ~14s          
   |++++++                                            | 10% ~14s          
   |++++++                                            | 11% ~14s          
   |+++++++                                           | 12% ~14s          
   |+++++++                                           | 13% ~14s          
   |++++++++                                          | 14% ~14s          
   |++++++++                                          | 15% ~14s          
   |+++++++++                                         | 16% ~13s          
   |+++++++++                                         | 17% ~13s          
   |++++++++++                                        | 18% ~13s          
   |++++++++++                                        | 19% ~13s          
   |+++++++++++                                       | 20% ~13s          
   |+++++++++++                                       | 21% ~13s          
   |++++++++++++                                      | 22% ~12s          
   |++++++++++++                                      | 23% ~12s          
   |+++++++++++++                                     | 24% ~12s          
   |+++++++++++++                                     | 25% ~12s          
   |++++++++++++++                                    | 26% ~12s          
   |++++++++++++++                                    | 27% ~12s          
   |+++++++++++++++                                   | 28% ~11s          
   |+++++++++++++++                                   | 29% ~11s          
   |++++++++++++++++                                  | 30% ~11s          
   |++++++++++++++++                                  | 31% ~11s          
   |+++++++++++++++++                                 | 32% ~11s          
   |+++++++++++++++++                                 | 33% ~11s          
   |++++++++++++++++++                                | 34% ~10s          
   |++++++++++++++++++                                | 35% ~10s          
   |+++++++++++++++++++                               | 36% ~10s          
   |+++++++++++++++++++                               | 37% ~10s          
   |++++++++++++++++++++                              | 38% ~10s          
   |++++++++++++++++++++                              | 39% ~10s          
   |+++++++++++++++++++++                             | 40% ~10s          
   |+++++++++++++++++++++                             | 41% ~09s          
   |++++++++++++++++++++++                            | 42% ~09s          
   |++++++++++++++++++++++                            | 43% ~09s          
   |+++++++++++++++++++++++                           | 44% ~09s          
   |+++++++++++++++++++++++                           | 45% ~09s          
   |++++++++++++++++++++++++                          | 46% ~09s          
   |++++++++++++++++++++++++                          | 47% ~08s          
   |+++++++++++++++++++++++++                         | 48% ~08s          
   |+++++++++++++++++++++++++                         | 49% ~08s          
   |++++++++++++++++++++++++++                        | 51% ~08s          
   |++++++++++++++++++++++++++                        | 52% ~08s          
   |+++++++++++++++++++++++++++                       | 53% ~08s          
   |+++++++++++++++++++++++++++                       | 54% ~07s          
   |++++++++++++++++++++++++++++                      | 55% ~07s          
   |++++++++++++++++++++++++++++                      | 56% ~07s          
   |+++++++++++++++++++++++++++++                     | 57% ~07s          
   |+++++++++++++++++++++++++++++                     | 58% ~07s          
   |++++++++++++++++++++++++++++++                    | 59% ~07s          
   |++++++++++++++++++++++++++++++                    | 60% ~06s          
   |+++++++++++++++++++++++++++++++                   | 61% ~06s          
   |+++++++++++++++++++++++++++++++                   | 62% ~06s          
   |++++++++++++++++++++++++++++++++                  | 63% ~06s          
   |++++++++++++++++++++++++++++++++                  | 64% ~06s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~06s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~05s          
   |++++++++++++++++++++++++++++++++++                | 67% ~05s          
   |++++++++++++++++++++++++++++++++++                | 68% ~05s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~05s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~05s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~04s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~04s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~04s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~03s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 16s
E15E16P1P4_wt_res14_2over7
E15E16P1P4_wt_res14_7over2<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(7),ident.2 = c(2),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~06s          
   |++                                                | 2 % ~06s          
   |++                                                | 3 % ~05s          
   |+++                                               | 4 % ~05s          
   |+++                                               | 5 % ~05s          
   |++++                                              | 6 % ~05s          
   |++++                                              | 8 % ~05s          
   |+++++                                             | 9 % ~05s          
   |+++++                                             | 10% ~05s          
   |++++++                                            | 11% ~05s          
   |++++++                                            | 12% ~05s          
   |+++++++                                           | 13% ~05s          
   |+++++++                                           | 14% ~05s          
   |++++++++                                          | 15% ~05s          
   |+++++++++                                         | 16% ~05s          
   |+++++++++                                         | 17% ~05s          
   |++++++++++                                        | 18% ~05s          
   |++++++++++                                        | 19% ~04s          
   |+++++++++++                                       | 20% ~04s          
   |+++++++++++                                       | 22% ~04s          
   |++++++++++++                                      | 23% ~04s          
   |++++++++++++                                      | 24% ~04s          
   |+++++++++++++                                     | 25% ~04s          
   |+++++++++++++                                     | 26% ~04s          
   |++++++++++++++                                    | 27% ~04s          
   |++++++++++++++                                    | 28% ~04s          
   |+++++++++++++++                                   | 29% ~04s          
   |++++++++++++++++                                  | 30% ~04s          
   |++++++++++++++++                                  | 31% ~04s          
   |+++++++++++++++++                                 | 32% ~04s          
   |+++++++++++++++++                                 | 33% ~04s          
   |++++++++++++++++++                                | 34% ~04s          
   |++++++++++++++++++                                | 35% ~04s          
   |+++++++++++++++++++                               | 37% ~04s          
   |+++++++++++++++++++                               | 38% ~03s          
   |++++++++++++++++++++                              | 39% ~03s          
   |++++++++++++++++++++                              | 40% ~03s          
   |+++++++++++++++++++++                             | 41% ~03s          
   |+++++++++++++++++++++                             | 42% ~03s          
   |++++++++++++++++++++++                            | 43% ~03s          
   |+++++++++++++++++++++++                           | 44% ~03s          
   |+++++++++++++++++++++++                           | 45% ~03s          
   |++++++++++++++++++++++++                          | 46% ~03s          
   |++++++++++++++++++++++++                          | 47% ~03s          
   |+++++++++++++++++++++++++                         | 48% ~03s          
   |+++++++++++++++++++++++++                         | 49% ~03s          
   |++++++++++++++++++++++++++                        | 51% ~03s          
   |++++++++++++++++++++++++++                        | 52% ~03s          
   |+++++++++++++++++++++++++++                       | 53% ~03s          
   |+++++++++++++++++++++++++++                       | 54% ~03s          
   |++++++++++++++++++++++++++++                      | 55% ~03s          
   |++++++++++++++++++++++++++++                      | 56% ~02s          
   |+++++++++++++++++++++++++++++                     | 57% ~02s          
   |++++++++++++++++++++++++++++++                    | 58% ~02s          
   |++++++++++++++++++++++++++++++                    | 59% ~02s          
   |+++++++++++++++++++++++++++++++                   | 60% ~02s          
   |+++++++++++++++++++++++++++++++                   | 61% ~02s          
   |++++++++++++++++++++++++++++++++                  | 62% ~02s          
   |++++++++++++++++++++++++++++++++                  | 63% ~02s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~02s          
   |++++++++++++++++++++++++++++++++++                | 67% ~02s          
   |++++++++++++++++++++++++++++++++++                | 68% ~02s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~02s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
   |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06s
E15E16P1P4_wt_res14_7over2

df_2_7_wt<-FetchData(seurat_E15E16P1P4_wt,c("Spdef","Creb3l1","Cited1","Dnajb9","Klk10","Klk13","Klk11","Muc5b","Muc1","Muc4","Gp2","Tff2","Sftpd","Scgb1a1","age","res.1.4"))
df_2_7_wt<-df_2_7_wt[order(df_2_7_wt$res.1.4,df_2_7_wt$age),]
age<-substr(rownames(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),]),1,3)
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

   0    1   10   11   12   13   14   15   16   17   18   19    2   20   21   22   23   24   25   26   27 
1347 1324  557  552  531  506  493  474  462  448  395  388 1094  362  342  317  272  245  222  221  209 
  28   29    3   30   31   32   33    4    5    6    7    8    9 
 196  149 1018  137  134   89   88  916  744  733  635  608  601 
library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c( "E16", "P1","P4"), # category labels
    col = c("#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)

library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))
Using scale="row" or scale="column" when breaks arespecified can produce unpredictable results.Please consider using only one or the other.
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c( "E16", "P1","P4"), # category labels
    col = c("#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)

ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(2,7),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ 
  scale_x_discrete(limits=c("2","7"))+
  scale_fill_manual(values = c("#2ca25f","#66c2a4","#99d8c9"))

seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Foxj1","Mcidas","Cdhr3","Creb3l1","Spdef","Gp2"), nCol = 3,x.lab.rot = T,point.size.use = 0.1,use.raw=F,group.by="specific_type",ident.include =c(2,7,20,27,28) )

immune response:
Toll like receptors and signaling:

CLRs (C-type lectin domain):

NLR:

RLR:

df_all_wt<-FetchData(seurat_E15E16P1P4_wt,c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1","res.1.4","age","seq_group","cell_type","Foxj1","Spdef","Foxa3","Creb3l1","Gp2","Tff2","Scgb1a1","Cftr","Foxi1","Gja1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4","Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1","Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg","Il2","Il17b","Il17d","Il34","Il11","Il15","Ifnk","Ifnlr1","Ifkbiz","Ifkbia"))
Error in FetchData(seurat_E15E16P1P4_wt, c("Lbp", "Cd14", "Tlr4", "Tlr2",  : 
  Error: Ifkbiz not found
table(colnames(df_all_wt))

      age    Alox15     Alox5      Areg     Ccl11     Ccl17      Ccl2     Ccl20    Ccl21a     Ccl24     Ccl25    Ccl27a     Ccl28      Ccl5      Ccl7      Cd14 
        1         1         1         1         1         1         1         1         1         1         1         1         1         2         1         1 
cell_type      Cftr     Chil4   Creb3l1    Cx3cl1     Cxcl1    Cxcl10    Cxcl12    Cxcl14    Cxcl15    Cxcl16    Cxcl17     Cxcl2     Ddx58     Defb1     Dhx58 
        1         1         1         1         1         1         1         1         1         1         1         1         1         1         1         1 
    F2rl1     Foxa3     Foxi1     Foxj1     Gata2      Gja1       Gp2     Hpgds     Ifih1      Il10      Il18      Il1b    Il1rl1      Il25      Il33       Il6 
        1         1         1         1         1         1         1         1         1         1         1         1         1         1         1         1 
    Itln1       Lbp      Lcn2    Lgals3       Ltf      Lyz2      Muc1     Muc16      Muc2     Muc20      Muc4    Muc5ac     Muc5b     Myd88     Nlrp6      Nod1 
        1         1         1         1         1         1         1         1         1         1         1         1         1         1         1         1 
     Nod2    Ormdl3       Pf4      Pigr     Ptgds     Ptges     Ptgs2     Reg3g   res.1.4    Retnla    S100a8    S100a9   Scgb1a1 seq_group    Sftpa1     Sftpb 
        1         1         1         1         1         1         1         1         1         1         1         1         1         1         1         1 
    Sftpd      Slpi     Spdef    Sucnr1    Tbxas1      Tff2     Tgfb1     Tgfb2    Ticam1      Tlr2      Tlr4       Tnf      Tslp 
        1         1         1         1         1         1         1         1         1         1         1         1         1 
#for (i in c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1"))
#{
#pdf(file = paste("Manuscript/MicrobialSensing/",i,".pdf", sep = ""), width = 7, height = 3)
#print(ggplot(df_all_wt[(df_all_wt$specific_type %in% #c("Basal","Secretory","Ciliated","Transitional","CiliaSecretory","CyclingEpithelial")),],aes_string(x="age",y=i))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.#01,stackdir="center",position=position_dodge(0.8), dotsize=0.8)+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
#                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1)))
#dev.off()
#}
Some categories of genes:
lipid synthetic enzymes or inhibitors: “Ormdl3”,“Ptges”,“Ptgds”,“Ptgs2”,“Hpgds”,“Hpgds”,“Ptgr2”,“Tbxas1”,“Alox5”,“Ptgdr”
known Th2 inducers, effectors, and mediators:“Il10”,“Tnf”,“S100a8”,“S100a9”,“Il6”,“Il18”,“Il1b”,“Il1rl1”,“Ccl11”,“Ccl24”,“Il33”,“Il25”,“Tslp”,“F2rl1”,“Retnla”,“Alox15”,“Alox5”,“Gata2”,“Tgfb2”,“Tgfb1”,“Ormdl3”,“Ptges”,“Ptgds”,“Ptgs2”,“Hpgds”,“Tbxas1”,“Areg”
chemokines: Pbpb not expressed, Ccl21c, Ccl21c.1, Cxcl11 not found in mm10.1.2.0. “Cxcl3”,“Cxcl2”,“Cxcl1”,“Pf4”,“Cxcl5”,“Cxcl9”,“Cxcl10”,“Cxcl12”,“Cxcl13”,“Cxcl14”,“Cxcl15”,“Cxcl16”,“Cxcl17”,“Ccl1”,“Ccl2”,“Ccl3”,“Ccl4”,“Ccl5”,“Ccl7”,“Ccl8”,“Ccl11”,“Ccl12”,“Ccl9”,“Ccl17”,“Ccl19”,“Ccl20”,“Ccl21a”,“Ccl21b.1”,“Ccl22”,“Ccl6”,“Ccl24”,“Ccl25”,“Ccl27a”,“Ccl27b”,“Ccl28”,“Xcl1”,“Cx3cl1”
expressed chemokines: “Ccl5”,“Cxcl10”,“Cxcl2”,“Cxcl1”,“Pf4”,“Cxcl12”,“Cxcl14”,“Cxcl15”,“Cxcl16”,“Cxcl17”,“Ccl2”,“Ccl7”,“Ccl17”,“Ccl20”,“Ccl21a”,“Ccl25”,“Ccl27a”,“Ccl28”,“Cx3cl1”
Interleukins (Il3,Il9,Il20,Il31 not expressed):“Il10”,“Il11”,“Il12a”,“Il12b”,“Il13”,“Il15”,“Il16”,“Il17a”,“Il17b”,“Il17c”,“Il17d”,“Il17f”,“Il18”,“Il19”,“Il2”,“Il21”,“Il22”,“Il24”,“Il25”,“Il27”,“Il33”,“Il34”,“Il4”,“Il5”,“Il6”,“Il7”,“Il1a”,“Il1b”
Antimicrobial effectors: “Muc1”,“Muc4”,“Muc16”,“Muc20”,“Muc5b”,“Muc5ac”,“Muc2”,“Defb1”,“Lyz2”,“Ltf”,“Sftpa1”,“Sftpd”,“Sftpb”,“Slpi”,“Lcn2”,“Pigr”,“Chil4”
known epithelial cytokines and mediators: “Il1a”,“Il25”,“Il33”,“Tslp”,“Csf2”,“Ccl5”,“Cxcl10”
Most interferons cannot be found in our dataset:

seurat_E15E16P1P4_wt@meta.data$type_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$cell_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))
seurat_E15E16P1P4_wt@meta.data$specificType_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g"),group.by = "ident", x.lab.rot = T,plot.legend = T)

print(levels(seurat_E15E16P1P4_wt@ident))
 [1] "Basal_E15"                 "Basal_E16"                 "Basal_P1"                  "Basal_P4"                 
 [5] "Chondrocyte_E15"           "Chondrocyte_E16"           "Chondrocyte_P1"            "Chondrocyte_P4"           
 [9] "CiliaSecretory_E16"        "CiliaSecretory_P1"         "CiliaSecretory_P4"         "Ciliated_E15"             
[13] "Ciliated_E16"              "Ciliated_P1"               "Ciliated_P4"               "CyclingEpithelial_E15"    
[17] "CyclingEpithelial_E16"     "CyclingEpithelial_P1"      "CyclingEpithelial_P4"      "CyclingFibroblast_E15"    
[21] "CyclingFibroblast_E16"     "CyclingFibroblast_P1"      "CyclingFibroblast_P4"      "Doublet_E16"              
[25] "Doublet_P1"                "Fibroblast_E15"            "Fibroblast_E16"            "Fibroblast_P1"            
[29] "Fibroblast_P4"             "Immune_1_E15"              "Immune_1_E16"              "Immune_1_P1"              
[33] "Immune_1_P4"               "Immune_2_E15"              "Immune_2_E16"              "Immune_2_P1"              
[37] "Immune_2_P4"               "LymphaticEndothelial_E15"  "LymphaticEndothelial_E16"  "LymphaticEndothelial_P1"  
[41] "LymphaticEndothelial_P4"   "MesenchymalProgenitor_E15" "MesenchymalProgenitor_E16" "MesenchymalProgenitor_P1" 
[45] "MesenchymalProgenitor_P4"  "Muscle_E15"                "Muscle_E16"                "Muscle_P1"                
[49] "Muscle_P4"                 "Neuron/NEC_E15"            "Neuron/NEC_E16"            "Neuron/NEC_P4"            
[53] "RBC_E16"                   "RBC_P1"                    "RBC_P4"                    "SchwannCell_E15"          
[57] "SchwannCell_E16"           "SchwannCell_P1"            "Secretory_E16"             "Secretory_P1"             
[61] "Secretory_P4"              "Thyroid_E15"               "Thyroid_E16"               "Thyroid_P1"               
[65] "Thyroid_P4"                "Transitional_E15"          "VascularEndothelial_E15"   "VascularEndothelial_E16"  
[69] "VascularEndothelial_P1"    "VascularEndothelial_P4"    "VSMC/pericyte_E15"         "VSMC/pericyte_E16"        
[73] "VSMC/pericyte_P1"          "VSMC/pericyte_P4"         
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("gray","forestgreen"),genes.plot = rev(c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g")),group.by = "ident", x.lab.rot = T,plot.legend = T,do.return = T)+rotate()+ theme(axis.text.x = element_text(angle = 45, vjust = 1,hjust=1)) #this scales both genotypes together
Factor `id` contains implicit NA, consider using `forcats::fct_explicit_na`

Mucosal chemokines (ccl25, ccl28, cxcl14, and cxcl17):

To look into genes correlated with scGPS scores:
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)
df_Ciliopathy_cor_test<-as.data.frame(do.call(rbind, Ciliopathy_cor_test))
df_order_Ciliopathy_cor_test<-df_Ciliopathy_cor_test[order(unlist(df_Ciliopathy_cor_test$estimate),decreasing = TRUE),]
tidy_Ciliopathy_cor<-cbind(df_order_Ciliopathy_cor_test$estimate,df_order_Ciliopathy_cor_test$p.value)
colnames(tidy_Ciliopathy_cor)<-c("cor","p.value")
tidy_Ciliopathy_cor
              cor        p.value      
Zmynd10       0.546579   0            
Pifo          0.5425509  0            
Lrrc6         0.5384109  0            
Drc1          0.5367434  0            
Dcdc2a        0.4935992  0            
Ccdc65        0.4863407  0            
Ccdc103       0.4782748  0            
Nek5          0.4693503  0            
Pacrg         0.4567388  0            
Traf3ip1      0.4471433  0            
Poc1a         0.4238989  0            
Fgfr1op       0.4206791  0            
Tmem138       0.4191797  0            
Ccdc114       0.4153728  0            
Tctex1d2      0.4115401  0            
Cenpf         0.407906   0            
Cep83         0.4070513  0            
Lrrc56        0.4045086  0            
Ift74         0.400825   0            
Cspp1         0.3996876  0            
Nphp1         0.395385   0            
Cep55         0.3951101  0            
Ift57         0.391189   0            
B9d1          0.3873108  0            
Ift88         0.3869675  0            
Rpgrip1l      0.3865638  0            
Dync2h1       0.3857778  0            
Ift172        0.381393   0            
Ift80         0.3790754  0            
Cep290        0.370438   0            
Plk4          0.368893   0            
Ift81         0.3683296  0            
Wdr34         0.3670231  0            
Arl6          0.3664624  0            
Cep41         0.3654999  0            
B9d2          0.3607634  0            
Ift43         0.3601675  0            
Tmem216       0.3597497  0            
Wdr60         0.3579426  0            
C2cd3         0.3569508  0            
Dync2li1      0.3564985  0            
Bbs5          0.3533716  0            
Wdr35         0.349725   0            
Tmem67        0.3472159  0            
Spag1         0.3449336  0            
Nek1          0.343219   0            
Tmem107       0.342606   0            
Wdpcp         0.3425418  0            
Sclt1         0.3367607  0            
Ift52         0.3292574  0            
Wdr19         0.3243224  0            
Kif11         0.3242046  0            
Cc2d2a        0.3237188  0            
2700049A03Rik 0.3222697  0            
Tmem231       0.320627   0            
Tmem17        0.3174036  0            
Cep164        0.3159639  0            
D430042O09Rik 0.3156484  0            
Bbip1         0.3148218  0            
2610301B20Rik 0.3137307  0            
Mks1          0.3134762  0            
Ift140        0.3058836  0            
Armc9         0.3034685  0            
Cep104        0.3006778  0            
1810043G02Rik 0.3004568  0            
Cep120        0.2943722  0            
Pibf1         0.2876425  1.388627e-317
Bbs7          0.2867671  1.392889e-315
Sdccag8       0.2848902  2.576083e-311
Ift122        0.2829121  7.454808e-307
Ift27         0.2670399  2.307436e-272
Bbs2          0.263533   4.716935e-265
Lztfl1        0.2576021  6.101379e-253
Ofd1          0.2527573  2.793616e-243
Intu          0.2493308  1.423027e-236
Inpp5e        0.2490544  4.894807e-236
Bbs1          0.2472615  1.424276e-232
Mre11a        0.2471985  1.882916e-232
Tbc1d32       0.2461905  1.618676e-230
Arl3          0.2422522  4.807888e-223
Poc1b         0.2354816  1.634973e-210
Mapkbp1       0.2330952  3.435638e-206
Fuz           0.2324085  5.899545e-205
Tctn3         0.223917   5.0765e-190  
Kif14         0.2098085  1.478587e-166
Pde6d         0.2009281  1.221831e-152
Notch1        0.1970841  8.116042e-147
Tctn1         0.1954091  2.56165e-144 
Mkks          0.1951724  5.752278e-144
Kctd10        0.1907798  1.571154e-137
Nphp3         0.1855321  4.787185e-130
Ccdc28b       0.1776045  3.662696e-119
Nek8          0.1751189  7.45169e-116 
Ttc21b        0.1615266  1.24399e-98  
Pik3r4        0.1607992  9.445133e-98 
Bbs12         0.1567567  6.200812e-93 
Sall1         0.1512815  1.296525e-86 
Gna12         0.1460858  7.835921e-81 
Galnt11       0.1320623  2.849657e-66 
Gli3          0.1303523  1.338961e-64 
Ahi1          0.1281622  1.719821e-62 
Tubgcp6       0.1242371  8.376372e-59 
Bbs10         0.1201034  4.783023e-55 
Sirt2         0.1090072  1.328542e-45 
Qk            0.1084995  3.413186e-45 
Wwtr1         0.1078645  1.104313e-44 
Fan1          0.09629114 6.398198e-36 
Sufu          0.08002379 2.726486e-25 
Kif7          0.07953328 5.317248e-25 
Glis2         0.0764992  3.024284e-23 
Zfp423        0.03725636 1.354483e-06 
Gucy2e        0.03294734 1.933375e-05 
unlist(tidy_Ciliopathy_cor[,1])
      Zmynd10.cor          Pifo.cor         Lrrc6.cor          Drc1.cor        Dcdc2a.cor        Ccdc65.cor 
       0.54657904        0.54255092        0.53841095        0.53674336        0.49359917        0.48634066 
      Ccdc103.cor          Nek5.cor         Pacrg.cor      Traf3ip1.cor         Poc1a.cor       Fgfr1op.cor 
       0.47827477        0.46935028        0.45673881        0.44714331        0.42389891        0.42067915 
      Tmem138.cor       Ccdc114.cor      Tctex1d2.cor         Cenpf.cor         Cep83.cor        Lrrc56.cor 
       0.41917967        0.41537280        0.41154005        0.40790602        0.40705129        0.40450856 
        Ift74.cor         Cspp1.cor         Nphp1.cor         Cep55.cor         Ift57.cor          B9d1.cor 
       0.40082497        0.39968759        0.39538503        0.39511007        0.39118900        0.38731078 
        Ift88.cor      Rpgrip1l.cor       Dync2h1.cor        Ift172.cor         Ift80.cor        Cep290.cor 
       0.38696745        0.38656376        0.38577781        0.38139299        0.37907543        0.37043802 
         Plk4.cor         Ift81.cor         Wdr34.cor          Arl6.cor         Cep41.cor          B9d2.cor 
       0.36889297        0.36832959        0.36702306        0.36646235        0.36549993        0.36076339 
        Ift43.cor       Tmem216.cor         Wdr60.cor         C2cd3.cor      Dync2li1.cor          Bbs5.cor 
       0.36016751        0.35974972        0.35794263        0.35695076        0.35649847        0.35337160 
        Wdr35.cor        Tmem67.cor         Spag1.cor          Nek1.cor       Tmem107.cor         Wdpcp.cor 
       0.34972497        0.34721589        0.34493360        0.34321905        0.34260603        0.34254181 
        Sclt1.cor         Ift52.cor         Wdr19.cor         Kif11.cor        Cc2d2a.cor 2700049A03Rik.cor 
       0.33676072        0.32925738        0.32432245        0.32420459        0.32371885        0.32226969 
      Tmem231.cor        Tmem17.cor        Cep164.cor D430042O09Rik.cor         Bbip1.cor 2610301B20Rik.cor 
       0.32062701        0.31740361        0.31596392        0.31564838        0.31482181        0.31373074 
         Mks1.cor        Ift140.cor         Armc9.cor        Cep104.cor 1810043G02Rik.cor        Cep120.cor 
       0.31347622        0.30588356        0.30346851        0.30067775        0.30045684        0.29437219 
        Pibf1.cor          Bbs7.cor       Sdccag8.cor        Ift122.cor         Ift27.cor          Bbs2.cor 
       0.28764247        0.28676713        0.28489023        0.28291214        0.26703987        0.26353297 
       Lztfl1.cor          Ofd1.cor          Intu.cor        Inpp5e.cor          Bbs1.cor        Mre11a.cor 
       0.25760209        0.25275731        0.24933081        0.24905441        0.24726149        0.24719847 
      Tbc1d32.cor          Arl3.cor         Poc1b.cor       Mapkbp1.cor           Fuz.cor         Tctn3.cor 
       0.24619054        0.24225224        0.23548163        0.23309521        0.23240846        0.22391701 
        Kif14.cor         Pde6d.cor        Notch1.cor         Tctn1.cor          Mkks.cor        Kctd10.cor 
       0.20980850        0.20092807        0.19708409        0.19540907        0.19517236        0.19077984 
        Nphp3.cor       Ccdc28b.cor          Nek8.cor        Ttc21b.cor        Pik3r4.cor         Bbs12.cor 
       0.18553207        0.17760446        0.17511892        0.16152657        0.16079922        0.15675672 
        Sall1.cor         Gna12.cor       Galnt11.cor          Gli3.cor          Ahi1.cor       Tubgcp6.cor 
       0.15128147        0.14608581        0.13206233        0.13035234        0.12816225        0.12423714 
        Bbs10.cor         Sirt2.cor            Qk.cor         Wwtr1.cor          Fan1.cor          Sufu.cor 
       0.12010344        0.10900717        0.10849955        0.10786448        0.09629114        0.08002379 
         Kif7.cor         Glis2.cor        Zfp423.cor        Gucy2e.cor 
       0.07953328        0.07649920        0.03725636        0.03294734 

#volc_Ciliopathy_cor<-mutate(tidy_Ciliopathy_cor,sig=ifelse(tidy_Ciliopathy_cor$p.value<0.01,"P_adj<0.01","Not Sig"))
df_tidy_ciliopathy_cor<-data.frame(matrix(unlist(tidy_Ciliopathy_cor), nrow=112, byrow=F),stringsAsFactors=FALSE)
colnames(df_tidy_ciliopathy_cor)<-c("cor","p.value")
df_tidy_ciliopathy_cor$gene<-rownames(tidy_Ciliopathy_cor)

unlist(x)[2:30]
    Cenpf     Cep55     Kif11   Tmem138      Plk4   Fgfr1op  Dync2li1     Ift57     Ift52     Nphp1     Ift74      Arl6  Tctex1d2 
0.9290814 0.9066218 0.8907960 0.8105366 0.8061935 0.8028617 0.7735900 0.7513981 0.7497918 0.7491373 0.7369705 0.7260828 0.7077285 
    Cspp1      Mkks    Cep164     Bbip1      B9d1     Pde6d     Ift43      Arl3    Kctd10   Tmem107     Sirt2        Qk    Lztfl1 
0.6831866 0.6693836 0.6540933 0.6537958 0.6483817 0.6434139 0.6353225 0.6175333 0.6173846 0.5910281 0.5530700 0.5435209 0.5248394 
    Armc9  Traf3ip1     Kif14 
0.0000000 0.0000000 0.0000000 
wt_mucosa_cluster<-data.frame(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],seurat_E15E16P1P4_wt@meta.data$res.1.4)
aggre_wt_mucosa_cluster<-aggregate(wt_mucosa_cluster[, 1:301], list(wt_mucosa_cluster[,302]), median)
unlist(sort(aggre_wt_mucosa_cluster[aggre_wt_mucosa_cluster$Group.1==8,],decreasing = TRUE))[2:51]
    Aurka     Epcam     Bub1b     Ap1m2      Cdh2     Krt17     Lamb3     Erbb2     Fgfr3     Trp53      Dkc1 
0.9465433 0.9464838 0.9262256 0.8715195 0.8370419 0.8083948 0.7931045 0.7698715 0.7693658 0.7673429 0.7644277 
      Dsp    Ctnnb1      Cdh1    Tmem97    Mapk14     Rab25      Sdc1     Fgfr2      Rela  Cdc42ep4      Nras 
0.7623156 0.7584781 0.7551761 0.7547596 0.7376547 0.7327166 0.7238517 0.7090374 0.7087994 0.6999048 0.6965136 
   Pycard      Med1   Zkscan3      Rnf6      Ece1      Yap1      Odc1      Klf6     Mark2     Smad7    Slc6a6 
0.6929438 0.6918729 0.6917539 0.6882437 0.6864291 0.6695621 0.6653974 0.6621549 0.6598644 0.6582580 0.6565326 
      Bax     Smad4     Stk11     Sar1b   Dnajc10      Pdpn    Nfkbiz     Bcl10   Ndufa13      Rps3     Mfge8 
0.6538256 0.6511483 0.6306521 0.6227094 0.5781473 0.5703832 0.5436994 0.5382853 0.5209127 0.5033615 0.5031830 
     Igf2      Akt1      Gnas    Nfkbia   Scgb3a2     Il17a 
0.4425274 0.4134043 0.4093586 0.4013267 0.3237149 0.0000000 
---
title: "Trachea_WT_10x"
output: html_notebook
---
## This script analyze 10x scRNA-seq data from E15, E16, P1, and P4 wt mice
```{r}
library(dplyr)
library(Matrix)
require(devtools)
install_version("mvtnorm", version = "1.0-8", repos = "http://cran.us.r-project.org") ##Note that I need version 1.0-8 because the newest version requires R>3.5 (my r version is 3.4). mvtnorm is a dependency for fpc which is a dependency for Seurat.
library(Seurat)
```

##### First load E15 data (nGene 1k, nUMI 5k already applied)
```{r}
load("E15_Oct10_10X_Trachea.RData")
E15_Oct10_mm10.1.2.0_Trachea <- SetAllIdent(object = E15_Oct10_mm10.1.2.0_Trachea, id = "genotype")

```
##### second load seu_E16P1P4_wt.RData
```{r}
load("seu_E16P1P4_wt.RData")

```
##### data has been log normalized.
```{r}
seurat_E15E16P1P4_wt<-MergeSeurat(object1 = seu_E16P1P4_wt,object2 = SubsetData(object=E15_Oct10_mm10.1.2.0_Trachea,ident.use=c("wt")),min.cells = 1,min.genes = 1,project="E15Oct10_E16Dec7_P1Dec11_P4Oct18")
```
##### remove old analysis done on the E16P1P4 dataset:
```{r}
seurat_E15E16P1P4_wt@meta.data<-seurat_E15E16P1P4_wt@meta.data[,-which(names(seurat_E15E16P1P4_wt@meta.data) %in% c("res.0.8", "res.1.2","res.1.4","cell_type","S.Score","G2M.Score","Phase","old.ident","CellCycle_score","mocosaGoblet_score","ciliopathy_score","PCD_score"))]
```
##### Basic stats about the data:
```{r}
aggregate(seurat_E15E16P1P4_wt@meta.data[, c(1:2,8:9)], list(seurat_E15E16P1P4_wt@meta.data$seq_group), median)

```
```{r}
median(seurat_E15E16P1P4_wt@meta.data$nGene) 
```
```{r}
mean(seurat_E15E16P1P4_wt@meta.data$nGene) 
```
```{r}
median(seurat_E15E16P1P4_wt@meta.data$nUMI) 
```
```{r}
mean(seurat_E15E16P1P4_wt@meta.data$nUMI) 
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$age) 
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$seq_group) 
```

##### scale the data:
```{r}
seurat_E15E16P1P4_wt <- ScaleData(object = seurat_E15E16P1P4_wt)
```

##### find variable genes and run PCA:
```{r}
seurat_E15E16P1P4_wt <- FindVariableGenes(object = seurat_E15E16P1P4_wt, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```
```{r}
seurat_E15E16P1P4_wt <- RunPCA(object = seurat_E15E16P1P4_wt,pcs.compute = 25, do.print = FALSE)
seurat_E15E16P1P4_wt <- ProjectPCA(object = seurat_E15E16P1P4_wt, do.print = FALSE)
```
```{r,fig.height=20,fig.width=8}
PCHeatmap(object = seurat_E15E16P1P4_wt, pc.use = c(1:12), cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 30)

```

```{r}
PCElbowPlot(object = seurat_E15E16P1P4_wt,num.pc = 25)
```
##### clustering:
##### try resolution=0.8:
```{r}
n.pcs = 24
res.used <- 0.8

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

```

```{r}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.0.8")

```

##### resolution=1.4:
```{r}
n.pcs = 24
res.used <- 1.4

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

##### k.param=15 to have better distinction for rare cells:
```{r}
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)

```

```{r}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.4")

```


###### res1.6 leads to further specification of immune cells, P1 and P4 mesenchymal progenitor separation, and separation of E16 basal:
```{r}
n.pcs = 24
res.used <- 1.6

seurat_E15E16P1P4_wt <- FindClusters(object = seurat_E15E16P1P4_wt, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
seurat_E15E16P1P4_wt <- RunTSNE(object = seurat_E15E16P1P4_wt, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=15)

```

```{r}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = T,pt.size = 0.2,group.by="res.1.6")
```

##### We will use resolution of 1.4 for further analysis.

####--------------------------------------------------------------------#################

```{r}
#save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData")  # this seurat object will be updated and saved as analysis goes
```

```{r}
#load("seuratE15E16P1P4_wt.RData")
```


```{r,fig.width=10,fig.height=5}

TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,pt.size = 0.2,group.by="age")

```
##### 2 E16 wt mice:
```{r,fig.width=10,fig.height=5}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,group.by="seq_group",pt.size = 0.2,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$age=="E16"])

```

##### 2 P1 wt mice:
```{r,fig.width=10,fig.height=5}
TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,group.by="seq_group",pt.size = 0.2,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$age=="P1"])

```


##### Annotate clusters by known markers:

```{r,fig.height=17,fig.width=50}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Krt4","Scgb3a2","Spdef","Creb3l1","Muc5b","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Fcer1g","Pecam1","Acta2","Myh11","Col11a1","Acan","Wnt2","Pi16","Ly6a","Twist2","Mki67","Top2a","Tg","Pax8"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 25,cex.row=30,group.order = c(3,8,11,9,1,30,2,20,29,7,26,27,28,14,31,23,18,21,13,15,19,16,10,12,6,4,17,0,5,33,22,25,24,32)
  )
```

```{r,fig.height=17,fig.width=50}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,8,11,9,1,30,2,20,29,7,26,27,28)],genes.use = c("Epcam","Trp63","Krt4","Scgb3a2","Spdef","Creb3l1","Muc5b","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Fcer1g","Pecam1","Acta2","Myh11","Col11a1","Acan","Wnt2","Pi16","Ly6a","Twist2","Mki67","Top2a","Tg","Pax8"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 25,cex.row=35,group.order = c(3,8,11,9,1,30,2,20,29,7,26,27,28)
  )
```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("doublet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```

```{r,fig.height=6,fig.width=15}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("doublet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type")

```
```{r, fig.height=5, fig.width=10}
FeaturePlot(seurat_E15E16P1P4_wt, c("Alas2","Hba-a2"),pt.size = 0.3, overlay=TRUE,no.legend=FALSE,cols.use=c("grey","red","blue","green"),max.cutoff = c(2.5,5))
```


```{r, fig.height=6, fig.width=12}
FeaturePlot(seurat_E15E16P1P4_wt, c("Tg","Pax8"),pt.size = 0.2,no.legend=FALSE,nCol = 2)
```
```{r, fig.height=5, fig.width=10}
FeaturePlot(seurat_E15E16P1P4_wt, c("Tg","Pax8"),pt.size = 0.3, overlay=TRUE,no.legend=FALSE,cols.use=c("grey","red","blue","green"),max.cutoff = c(2.5,1))
```

##### cluster annotation v1:
```{r}
library(plyr)
seurat_E15E16P1P4_wt@meta.data$cell_type<-mapvalues(seurat_E15E16P1P4_wt@meta.data$res.1.4,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32","33"),to=c("Fibroblast","Basal","Secretory","Basal","Fibroblast","Fibroblast","Fibroblast","Secretory","CyclingEpithelial","Transitional","Fibroblast","CyclingEpithelial","Fibroblast","Fibroblast","Immune","Fibroblast","Fibroblast","Fibroblast","Muscle","Fibroblast","Ciliated","Muscle","MesenchymalProgenitor","Endothelial","Thyroid","MesenchymalProgenitor","Neural/Schwann","Ciliated","CiliaSecretory","Basal","CyclingEpithelial","Endothelial","Doublet","MesenchymalProgenitor"))
```

##### use nonShh subset clustering information (seurat object: E15E16P1P4_wt_nonShh resolution=1.6) to annotate those non-epithelial cells:
```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_nonShh_cellType1.6, col.name = "specific_type")
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$specific_type)
```

##### keep the epithelial cell annotation as in seurat_E15E16P1P4_wt@meta.data$cell_type:
```{r}
seurat_E15E16P1P4_wt@meta.data$specific_type <- ifelse(is.na(seurat_E15E16P1P4_wt@meta.data$specific_type), as.character(seurat_E15E16P1P4_wt@meta.data$cell_type), as.character(seurat_E15E16P1P4_wt@meta.data$specific_type))
```

##### now we have new (more detailed) annotation for all cells:
```{r}
table(seurat_E15E16P1P4_wt@meta.data$specific_type)
```

```{r}
table(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$gate,useNA = "always")  
#gate <NA> means cells from E16 and P1 which did not go through FACS
```
##### specific_type of each cell is defined later.
```{r,fig.width=10,fig.height=5}

TSNEPlot(object = seurat_E15E16P1P4_wt, do.label = F,pt.size = 0.2,group.by="specific_type")+scale_color_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#000000', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#808080', '#ffffff'
))

```
```{r,fig.width=5,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("E15"),],aes(gate,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#4363d8', '#f58231', '#911eb4', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#000000', '#800000', '#808000', '#ffd8b1', '#000075', '#808080', '#ffffff'
))  
```


```{r,fig.width=5,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("P4"),],aes(gate,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#000000', '#fffac8', '#aaffc3', '#808000', '#000075', '#808080', '#ffffff'
))
```
```{r,fig.width=10,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data,aes(specific_type,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```



```{r,fig.width=5,fig.height=8}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("P1"),],aes(seq_group,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#000075', '#808080', '#ffffff'
))
```
```{r,fig.width=5,fig.height=8}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$age %in% c("E16"),],aes(seq_group,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324','#000000', '#fffac8', '#800000', '#aaffc3', '#808000', '#000075', '#808080', '#ffffff'
))
```


```{r}
save(seurat_E15E16P1P4_wt,file="seuratE15E16P1P4_wt.RData")
```

```{r,fig.height=17,fig.width=90}
DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Krt5","Krt8","Scgb3a2","Spdef","Creb3l1","Gp2","Foxj1","Twist2","Wnt2","Ly6a","Thy1","Sox9","Acan","Acta2","Myh11","Rgs5","Notch3","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="specific_type",group.cex = 30,cex.row=30,cells.use = seurat_E15E16P1P4_wt@cell.names[!(seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Thyroid","Doublet","RBC"))],group.order = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC")
  )
```


##### find differentially expressed genes between clusters of interest:
```{r}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

E15E16P1P4_wt_res14_BasalSecretory<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(9,30),only.pos = TRUE)
E15E16P1P4_wt_res14_BasalSecretory
```

##### For the purpose of visualization, we average within each specific cell type:
```{r}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
average_wt_specific_Annotation<-AverageExpression(object = seurat_E15E16P1P4_wt,return.seurat = T)
```
```{r,fig.height=12,fig.width=15}

DoHeatmap(object = average_wt_specific_Annotation, genes.use = c("Epcam","Trp63","Krt5","Krt8","Scgb3a2","Spdef","Creb3l1","Gp2","Foxj1","Twist2","Wnt2","Ly6a","Thy1","Sox9","Acan","Acta2","Myh11","Rgs5","Notch3","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,cells.use = average_wt_specific_Annotation@cell.names[!(average_wt_specific_Annotation@cell.names %in% c("Thyroid","Doublet","RBC"))],group.order = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))
```

```{r,fig.height=6,fig.width=12}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
# just to plot different cell types by a custom order:
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("lightgrey","red"),genes.plot = c("Ano1","Cftr","Lrrc8a","Clca3a1","Clca3a2","Clca3b","Clca4a","Clca4b","Clcc1","Clcn2","Clcn3","Clcn4","Clcn7","Clcnka","Clcnkb","Clca1","Clca2","Gabrp","Ano2","Clcn1","Clcn5","Clic3"),group.by = "ident", x.lab.rot = T,plot.legend = T) # other chloride channels
```
```{r}
print(levels(seurat_E15E16P1P4_wt@ident))
```

```{r,fig.height=6,fig.width=12}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("lightgrey","red"),genes.plot = c("Mki67","Epcam","Trp63","Muc5ac","Spdef","Creb3l1","Gp2","Foxj1","Snap25","Chga","Plp1","Mpz","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Acta2","Myh11","Rgs5","Notch3","Wnt2","Ly6a","Thy1","Cd34","Acan","Col2a1","Twist2"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

##### epithlial cells across ages:
```{r,fig.height=5, fig.width=15}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Trp63","Spdef","Creb3l1","Foxj1","CellCycle_score"), nCol = 5,x.lab.rot = T,point.size.use = 0.2,ident.include = c(11,8,3,9,1,29,30,2,7,20,27,28),group.by="age", legend.position = "left")
```

```{r,fig.height=5, fig.width=8}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Cftr","Ano1"), nCol = 2,x.lab.rot = T,point.size.use = 0.2,ident.include = c(11,8,3,9,1,29,30,2,7,20,27,28),group.by="age", legend.position = "left")
```

```{r}
df_wt<-FetchData(seurat_E15E16P1P4_wt,c("Ano1","Cftr","Krt4","Krt13","res.1.4","age","seq_group","cell_type","specific_type"))

```

```{r, fig.height=3, fig.width=10}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(specific_type,Cftr))+facet_grid(.~age)+geom_dotplot(binaxis="y",aes(color=age,fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```
```{r, fig.height=3, fig.width=3}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,Cftr))+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.07,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))
```
```{r, fig.height=3, fig.width=3}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,Ano1))+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.07,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))
```
```{r, fig.height=3, fig.width=10}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(specific_type,Ano1))+facet_grid(.~age)+geom_dotplot(binaxis="y",aes(color=age,fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```

```{r, fig.height=3, fig.width=10}
ggplot(df_wt[df_wt$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,Ano1))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(color=age,fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```

##### any possible ionocytes?:
```{r,fig.height=5, fig.width=15}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Cftr","Foxi1"), nCol = 2,x.lab.rot = T,point.size.use = 0.2,ident.include = c(11,8,3,9,1,29,30,2,7,20,27,28),group.by="age", legend.position = "left")
```
```{r}
table(df_all_wt$res.1.4[df_all_wt$Foxi1>0])

```

```{r}
table(df_all_wt$res.1.4[df_all_wt$Cftr>0 & df_all_wt$Foxi1>0])

```
##### epithelial cell composition across ages:
```{r,fig.width=5,fig.height=5}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28),],aes(age,fill=specific_type))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r}
table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)],seurat_E15E16P1P4_wt@meta.data$cell_type[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(11,8,3,9,1,29,30,2,7,20,27,28)])
```

##### a few lineage markers:
```{r,fig.height=20,fig.width=20}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Sox10","Mpz","Shh","Phox2a","Phox2b"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type")

```

```{r,fig.height=8,fig.width=22}
seurat_E15E16P1P4_wt=buildClusterTree(seurat_E15E16P1P4_wt,do.reorder = F,reorder.numeric = F,pcs.use = 1:24)

```

##### potential mesenchymal progenitors:
```{r}
E15E16P1P4_wt_res14_22over33<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(22),ident.2 = c(33),only.pos = TRUE)
E15E16P1P4_wt_res14_22over33
```
```{r}
E15E16P1P4_wt_res14_33over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(33),ident.2 = c(22),only.pos = TRUE)
E15E16P1P4_wt_res14_33over22
```
```{r}
E15E16P1P4_wt_res14_25over22<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(25),ident.2 = c(22),only.pos = TRUE)
E15E16P1P4_wt_res14_25over22
```

```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Piezo2"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=4,fig.width=47}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Sox9","Acan","Col2a1","Piezo2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 40,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(14,31,23,18,21,13,15,19,16,10,12,6,4,17,0,5,33,22,25)],group.order = c(10,33,12,6,0,22,5,4,13,18,17,25,15,19,21,16,14,31,23)
  )
```

```{r,fig.height=15,fig.width=46}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Wnt2","Cd34","Prmt1","Egfl6","Crabp2","Piezo2","Reg3g","Ybx3","Klf9","Zfp36","Lpl","Ptrf","Ly6a","Tppp3","Anxa1","Ly6c1","Pi16","Thy1","Anxa3","Ccl7","Ccl2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 40,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(33,22,25)],group.order = c(33,22,25)
  )
```

```{r,fig.height=15,fig.width=46}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Wnt2","Cd34","Prmt1","Egfl6","Crabp2","Piezo2","Sostdc1","Ybx3","Klf9","Zfp36","Ptrf","Lpl","Ly6a","Tppp3","Ly6c1","Pi16","Thy1","Anxa3","Anxa1","Ccl7","Ccl2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="age",group.cex = 40,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(33,22,25)]
  )
```

##### scGPS (scoring):

```{r}
geneList<-read.csv(file = "GenesOMIM_all.txt",header=T,sep="\t",stringsAsFactors = F) 
# Genes_scGPS_all.txt and GenesOMIM_all.txt are essentially the same. Both files can be used here.
geneList<-lapply(geneList,function(x) unlist(strsplit(unlist(x),split=","))) 
head(geneList$Mucociliary)
```


```{r}
library(dplyr)
percentile_table_E15E16P1P4wt<-apply(seurat_E15E16P1P4_wt@data,1,percent_rank)
```
```{r}
percentile_table_E15E16P1P4wt[1:6,1:6]  #just to take a look at the percent_rank

```
```{r}
 CellCycle_score_wt<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Cell_cycle],1,mean)
```
```{r}
 head( CellCycle_score_wt)
```
```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = CellCycle_score_wt, col.name = "CellCycle_score")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("CellCycle_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r}
wt_mocosaGoblet_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],1,mean)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_mocosaGoblet_score, col.name = "mucosaGoblet_score")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4",do.sort = T)

```


```{r}
 wt_ciliopathy_table<- percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Ciliopathy]
```
```{r}
 wt_ciliopathy_score<- apply(wt_ciliopathy_table,1,mean)
```
```{r}
 head(wt_ciliopathy_score)
```
```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_ciliopathy_score, col.name = "ciliopathy_score")

```
```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("ciliopathy_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r}
 wt_PCD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Primary.ciliary.dyskinesia],1,mean)
```
```{r}
 head(wt_PCD_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_PCD_score, col.name = "PCD_score")

```

```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```

```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```

```{r, fig.height=5, fig.width=5}

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Ciliated","CiliaSecretory"),],aes(age,PCD_score))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.01,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
```




```{r,fig.height=5,fig.width=3}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("PCD_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",do.sort = F,ident.include = c("CiliaSecretory"))

```

```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```
```{r,fig.height=5,fig.width=3}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",do.sort = F,ident.include = c("Secretory"))

```
```{r,fig.height=5,fig.width=3}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("mucosaGoblet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",do.sort = F,ident.include = c("CiliaSecretory"))

```

```{r, fig.height=5, fig.width=5}

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Secretory","CiliaSecretory"),],aes(age,mucosaGoblet_score))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.01,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
```

```{r}
 wt_bronchitis_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Bronchiectasis.and.Bronchitis],1,mean)
```
```{r}
 head(wt_bronchitis_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_bronchitis_score, col.name = "Bronchiectasis_Bronchitis")

```

```{r,fig.height=5,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Bronchiectasis_Bronchitis"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Bronchiectasis_Bronchitis"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```



```{r}
 wt_COPD_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$COPD],1,mean)
```
```{r}
 head(wt_COPD_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_COPD_score, col.name = "COPD")

```


```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("COPD"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```

```{r}
 wt_asthma_score<- apply(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Pulmonary...Asthma],1,mean)
```
```{r}
 head(wt_asthma_score)
```

```{r}
seurat_E15E16P1P4_wt<-AddMetaData(object = seurat_E15E16P1P4_wt, metadata = wt_asthma_score, col.name = "Asthma")

```

```{r,fig.height=5,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(seurat_E15E16P1P4_wt,id="specific_type")
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Asthma"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="specific_type",do.sort = F,ident.include = c("CyclingEpithelial","Basal","Transitional","Secretory","Ciliated","CiliaSecretory","Fibroblast","CyclingFibroblast","MesenchymalProgenitor","Chondrocyte","Muscle","VSMC/pericyte","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"))

```
```{r, fig.height=5, fig.width=7}

ggplot(seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$specific_type %in% c("Secretory","CiliaSecretory","Immune_1"),],aes(age,Asthma))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.005,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+stat_compare_means(comparisons = list(c("E16", "P1"),c("P1", "P4"),c("E16", "P4")),method="wilcox.test",size=4,label="p.adj")+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1),strip.text.x = element_text(size = 9, colour = "black", angle = 0))
```

##### check some human GWAS:

```{r,fig.height=6,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Apip","Hhip","Chrna5","Htr4","Adgrg6","Thsd4","Fam13a","Gstcd","Rin3","Adam19","Tet2","Eefsec","Cfdp1","Tgfb2","Ager","Sgf29","Armc2","Pid1","Dsp","Mtcl1","Rarb","Sftpd","Cyp2a4","Cyp2a5"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```


##### CF GWAS:
```{r,fig.height=4,fig.width=6}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Muc4","Muc20","Agtr2","Slc6a14","Ehf","Aplp1","Aplp2","H2-Aa","H2-Ab1","H2-Eb1","H2-Eb2"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

##### COPD:
```{r,fig.height=4,fig.width=9}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")
seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Hhip","Chrna5","Htr4","Adgrg6","Thsd4","Fam13a","Gstcd","Rin3","Adam19","Tet2","Eefsec","Cfdp1","Tgfb2","Ager","Sgf29","Armc2","Pid1","Dsp","Mtcl1","Rarb","Sftpd","Cyp2a4","Cyp2a5"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

##### Developmental lanscape:
##### basal cells across time:
```{r}
seurat_E15E16P1P4_wt <- SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

E15E16P1P4_wt_res14_3over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(3),ident.2 = c(1),only.pos = TRUE)
E15E16P1P4_wt_res14_3over1
```

```{r}
write.table(E15E16P1P4_wt_res14_3over1,"E15E16P1P4_wt_res14_3over1.txt",sep="\t")

```


```{r}
E15E16P1P4_wt_res14_1over3<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(1),ident.2 = c(3),only.pos = TRUE)
E15E16P1P4_wt_res14_1over3
```

```{r}
write.table(E15E16P1P4_wt_res14_1over3,"E15E16P1P4_wt_res14_1over3.txt",sep="\t")

```


```{r}
E15E16P1P4_wt_res14_29over1<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(29),ident.2 = c(1),only.pos = TRUE)
E15E16P1P4_wt_res14_29over1
```

```{r,fig.height=15,fig.width=40}

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Wnt6","Wnt4","Bmp7","Krt15","Krt5","Cyp2f2","Lbp","BC048546","Ly6e","Aqp3","Aqp4","Wnt4","Retnla","Itln1","Ltf","Scgb1a1","Aqp5","Errfi1","Epas1","Rpl9-ps6","Rpl13-ps3","Rpl6l"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 50,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,8,11,9,1,30,2,20,29,7,26,27,28,24)],group.order = c(3,8,11,9,1,30,2,20,29,7,27,28,26,24)
  )
```

```{r,fig.height=28, fig.width=12}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Aqp1","Aqp2","Aqp3","Aqp4","Aqp5","Aqp6","Aqp7","Aqp8","Aqp9","Aqp11","Aqp12"), nCol = 1,x.lab.rot = T,point.size.use = 0.2)
```

```{r,fig.height=6,fig.width=20}
DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Krt5","Krt15","Krt14","Wnt4","Bmp7","Aqp3","Aqp4","Aqp5","Errfi1","Rpl6l","Rpl9-ps6","Rpl13-ps3"),
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 40,cex.row=25,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,1,29)],group.order = c(3,1,29)
  )
```
```{r}
df_3_1_29_wt<-FetchData(seurat_E15E16P1P4_wt,c("Trp63","Cldn6","Nnat","Id2","Id3","Wnt7b","Wnt4","Krt5","Krt15","Aqp3","Aqp4","Aqp5","Rpl6l","Rpl9-ps6","Rpl13-ps3","age","res.1.4"))
df_3_1_29_wt<-df_3_1_29_wt[order(factor(df_3_1_29_wt$res.1.4,levels=c("3","1","29")),df_3_1_29_wt$age),]
```
```{r}
age<-substr(rownames(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),]),1,3)

```

```{r}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

```

```{r}
library(gplots)
library(viridis)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r}
library(gplots)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r, fig.height=3, fig.width=7}

pdf(file = paste("Manuscript/heatmap/","basal_dev",".pdf", sep = ""), width = 7, height = 3)
heatmap.2(t(as.matrix(df_3_1_29_wt[df_3_1_29_wt$res.1.4 %in% c(3,1,29),1:15])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1018,2342),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
dev.off()

```


```{r,fig.width=3,fig.height=4}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(3,1,29),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_fill_manual(values = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"))+scale_x_discrete(limits=c("3","1","29"))
```

##### ciliated cells across time:
```{r}
E15E16P1P4_wt_res14_27over20<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(27),ident.2 = c(20),only.pos = TRUE)
E15E16P1P4_wt_res14_27over20
```
```{r}
write.table(E15E16P1P4_wt_res14_27over20,"E15E16P1P4_wt_res14_27over20.txt",sep="\t")

```
```{r}
E15E16P1P4_wt_res14_20over27<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(20),ident.2 = c(27),only.pos = TRUE)
E15E16P1P4_wt_res14_20over27
```
```{r}
write.table(E15E16P1P4_wt_res14_20over27,"E15E16P1P4_wt_res14_20over27.txt",sep="\t")

```

```{r,fig.height=20,fig.width=40}

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Epcam","Cox6b2","Cks2","H2afx","Ccna1","Ccno","Plk4","Cdc20b","Cdc20","Foxn4","Shisa8","Hyls1","Smim24","Mcidas","Ift80","Prr18","Sntn","Stmnd1","Ldlrad1","Cdhr4","Cdhr3","Slc23a2","Basp1","Fam166b","Ifitm1","Ly6a","Ly6e","Ly6c1","Adam8","Cxcl17","Aoc1","Retnla","Itln1","Ltf","Prr18"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 50,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],group.order = c(20,27)
  )
```

```{r,fig.height=20,fig.width=40}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)
```

```{r}
df_20_27_wt<-FetchData(seurat_E15E16P1P4_wt,c("Foxj1","Lrrc23","Ccdc67","Cep152","Plk4","Ccna1","Ccno","Cdc20b","Cdc20","Hyls1","Mcidas","Smim24","Ift80","Prr18","Sntn","Stmnd1","Ldlrad1","Cdhr4","Cdhr3","Slc23a2","Ly6a","Ly6c1","Adam8","Cxcl17","Ifitm1","age","res.1.4"))
df_20_27_wt<-df_20_27_wt[order(df_20_27_wt$res.1.4,df_20_27_wt$age),]
```
```{r}
age<-substr(rownames(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),]),1,3)

```


```{r}
library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:25])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```
```{r}
library(gplots)
heatmap.2(t(as.matrix(df_20_27_wt[df_20_27_wt$res.1.4 %in% c(20,27),1:22])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#006d2c","#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(362),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c("E15", "E16", "P1","P4"), # category labels
    col = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r,fig.width=3,fig.height=4}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ 
  scale_x_discrete(limits=c("20","27"))+
  scale_fill_manual(values = c("#006d2c","#2ca25f","#66c2a4","#99d8c9"))
```

```{r,fig.width=5,fig.height=5}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)])
```
```{r,fig.width=5,fig.height=5}
t_ciliated<-as.data.frame(prop.table(table(seurat_E15E16P1P4_wt@meta.data$age[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)],seurat_E15E16P1P4_wt@meta.data$res.1.4[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(20,27)]),1))
```
```{r,fig.width=3,fig.height=2}
ggplot(data=t_ciliated,aes(x=Var1,y=Freq,group=Var2,colour=Var2))+geom_line()+geom_point()+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r,fig.width=3,fig.height=2}
ggplot(data=t_ciliated,aes(x=substr(Var1,1,1),y=Freq,group=Var2))+geom_line(aes(colour=Var2),linetype="dotted")+geom_point(aes(shape=Var1))+ theme(axis.text.x = element_text(angle = 45, hjust = 0.7))+
  scale_shape_manual(values = c("square open", "cross","diamond open","plus")) 
```

##### secretory cells across time:
```{r}
E15E16P1P4_wt_res14_2over7<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(2),ident.2 = c(7),only.pos = TRUE)
E15E16P1P4_wt_res14_2over7
```
```{r}
E15E16P1P4_wt_res14_7over2<-FindMarkers(seurat_E15E16P1P4_wt,ident.1=c(7),ident.2 = c(2),only.pos = TRUE)
E15E16P1P4_wt_res14_7over2
```

```{r,fig.height=8,fig.width=40}

DoHeatmap(object = seurat_E15E16P1P4_wt, genes.use = c("Spdef","Creb3l1","Cited1","Dnajb9","Klk10","Klk13","Klk11","Muc5ac","Muc5b","Muc1","Muc4","Gp2","Tff2","Sftpd","Scgb1a1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 50,cex.row=40,cells.use = seurat_E15E16P1P4_wt@cell.names[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(2,7)]
  )
```
```{r}
df_2_7_wt<-FetchData(seurat_E15E16P1P4_wt,c("Spdef","Creb3l1","Cited1","Dnajb9","Klk10","Klk13","Klk11","Muc5b","Muc1","Muc4","Gp2","Tff2","Sftpd","Scgb1a1","age","res.1.4"))
df_2_7_wt<-df_2_7_wt[order(df_2_7_wt$res.1.4,df_2_7_wt$age),]
```
```{r}
age<-substr(rownames(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),]),1,3)

```

```{r}
table(seurat_E15E16P1P4_wt@meta.data$res.1.4)

```

```{r}
library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=plasma(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c( "E16", "P1","P4"), # category labels
    col = c("#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```
```{r}
library(gplots)
heatmap.2(t(as.matrix(df_2_7_wt[df_2_7_wt$res.1.4 %in% c(2,7),1:14])),col=cividis(500), scale="row",Colv = NA, Rowv = NA,ColSideColors=c("#2ca25f","#66c2a4","#99d8c9")[as.numeric(as.factor(age))],labCol =NA,density.info="none",trace="none",dendrogram='none',srtCol=45,cexCol = 1,colsep=c(1094),breaks=seq(-3,3,length.out=501))
par(lend = 1)           # square line ends for the color legend
legend("left",      # location of the legend on the heatmap plot
    legend = c( "E16", "P1","P4"), # category labels
    col = c("#2ca25f","#66c2a4","#99d8c9"),  # color key
    lty= 1,             # line style
    lwd = 10            # line width
)
```

```{r,fig.width=3,fig.height=4}
ggplot(data=seurat_E15E16P1P4_wt@meta.data[seurat_E15E16P1P4_wt@meta.data$res.1.4 %in% c(2,7),],aes(res.1.4,fill=age))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+ 
  scale_x_discrete(limits=c("2","7"))+
  scale_fill_manual(values = c("#2ca25f","#66c2a4","#99d8c9"))
```


```{r,fig.height=8,fig.width=8}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")

VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Foxj1","Mcidas","Cdhr3","Creb3l1","Spdef","Gp2"), nCol = 3,x.lab.rot = T,point.size.use = 0.1,use.raw=F,group.by="specific_type",ident.include =c(2,7,20,27,28) )

```
```{r,fig.height=4,fig.width=3}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("doublet_score"), nCol = 1,x.lab.rot = T,point.size.use = 0.1,use.raw=F,group.by="specific_type",ident.include =c(2,7,20,27,28) )

```
##### immune response:
##### Toll like receptors and signaling:
```{r,fig.height=40,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Tlr1","Tlr2","Tlr3","Tlr4","Tlr5","Tlr6","Tlr7","Tlr8","Tlr9","Tlr11","Tlr12","Tlr13","Myd88","Ticam1"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```

#####CLRs (C-type lectin domain): 
```{r,fig.height=10,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Reg3a","Reg3b","Reg3g","Clec4n"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
##### NLR:
```{r,fig.height=6,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Nod1","Nod2","Nlrp6"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
##### RLR:
```{r,fig.height=6,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Ddx58","Ifih1","Dhx58"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```


```{r,fig.height=20,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Cd177","Ptgdr","F2rl1","Tslp","Il33","Il25","Ager"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```
```{r,fig.height=18,fig.width=25}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Defb1","Lyz1","Lyz2","Ltf","Camp","Sftpa1","Sftpd","Slpi"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="res.1.4")

```



```{r}
df_all_wt<-FetchData(seurat_E15E16P1P4_wt,c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1","res.1.4","age","seq_group","specific_type","Foxj1","Spdef","Foxa3","Creb3l1","Gp2","Tff2","Scgb1a1","Cftr","Foxi1","Gja1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4","Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1","Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg","Il2","Il17b","Il17d","Il34","Il11","Il15","Ifnk","Ifnlr1","Nfkbiz","Nfkbia"))

```
```{r}
table(colnames(df_all_wt))
```

```{r, fig.height=3, fig.width=7}
#for (i in c("Lbp","Cd14","Tlr4","Tlr2","Myd88","Ticam1","Itln1","Reg3g","Lgals3","Nod1","Nod2","Nlrp6","Ddx58","Ifih1","Dhx58","Sucnr1"))
#{
#pdf(file = paste("Manuscript/MicrobialSensing/",i,".pdf", sep = ""), width = 7, height = 3)
#print(ggplot(df_all_wt[(df_all_wt$specific_type %in% #c("Basal","Secretory","Ciliated","Transitional","CiliaSecretory","CyclingEpithelial")),],aes_string(x="age",y=i))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.#01,stackdir="center",position=position_dodge(0.8), dotsize=0.8)+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
#                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1)))
#dev.off()
#}
```

##### Some categories of genes:
##### lipid synthetic enzymes or inhibitors: "Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Hpgds","Ptgr2","Tbxas1","Alox5","Ptgdr"
##### known Th2 inducers, effectors, and mediators:"Il10","Tnf","S100a8","S100a9","Il6","Il18","Il1b","Il1rl1","Ccl11","Ccl24","Il33","Il25","Tslp","F2rl1","Retnla","Alox15","Alox5","Gata2","Tgfb2","Tgfb1","Ormdl3","Ptges","Ptgds","Ptgs2","Hpgds","Tbxas1","Areg"
##### chemokines: Pbpb not expressed, Ccl21c, Ccl21c.1, Cxcl11 not found in mm10.1.2.0. "Cxcl3","Cxcl2","Cxcl1","Pf4","Cxcl5","Cxcl9","Cxcl10","Cxcl12","Cxcl13","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl1","Ccl2","Ccl3","Ccl4","Ccl5","Ccl7","Ccl8","Ccl11","Ccl12","Ccl9","Ccl17","Ccl19","Ccl20","Ccl21a","Ccl21b.1","Ccl22","Ccl6","Ccl24","Ccl25","Ccl27a","Ccl27b","Ccl28","Xcl1","Cx3cl1"
##### expressed chemokines: "Ccl5","Cxcl10","Cxcl2","Cxcl1","Pf4","Cxcl12","Cxcl14","Cxcl15","Cxcl16","Cxcl17","Ccl2","Ccl7","Ccl17","Ccl20","Ccl21a","Ccl25","Ccl27a","Ccl28","Cx3cl1"
##### Interleukins (Il3,Il9,Il20,Il31 not expressed):"Il10","Il11","Il12a","Il12b","Il13","Il15","Il16","Il17a","Il17b","Il17c","Il17d","Il17f","Il18","Il19","Il2","Il21","Il22","Il24","Il25","Il27","Il33","Il34","Il4","Il5","Il6","Il7","Il1a","Il1b"
##### Antimicrobial effectors: "Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Defb1","Lyz2","Ltf","Sftpa1","Sftpd","Sftpb","Slpi","Lcn2","Pigr","Chil4"
##### known epithelial cytokines and mediators: "Il1a","Il25","Il33","Tslp","Csf2","Ccl5","Cxcl10"
##### Most interferons cannot be found in our dataset:

```{r,fig.height=4, fig.width=15}
VlnPlot(object = seurat_E15E16P1P4_wt, features.plot = c("Ifnk","Ifnlr1"),group.by="age", legend.position = "left")
```
```{r}
seurat_E15E16P1P4_wt@meta.data$type_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$cell_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))
```
```{r}
seurat_E15E16P1P4_wt@meta.data$specificType_age<-as.factor(paste(seurat_E15E16P1P4_wt@meta.data$specific_type,seurat_E15E16P1P4_wt@meta.data$age,sep="_"))
```


```{r,fig.height=6,fig.width=12}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specific_type")

seurat_E15E16P1P4_wt@ident = factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(5,1,19,17,4,3,14,16,20,11,9,10,13,21,12,2,8,6,18,15,7)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("forestgreen","magenta3"),genes.plot = c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r}
print(levels(seurat_E15E16P1P4_wt@ident))
```

```{r,fig.height=10,fig.width=10}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "specificType_age")
seurat_E15E16P1P4_wt@ident=factor(seurat_E15E16P1P4_wt@ident,levels(seurat_E15E16P1P4_wt@ident)[c(16:19,1:4,66,59:61,12:15,9:11,30:37)])
DotPlot(object = seurat_E15E16P1P4_wt, cols.use = c("gray","forestgreen"),genes.plot = rev(c("Ccl20","Ptgds","Ptges","Ptgs2","Cxcl15","Cxcl17","Ccl28","Retnla","Nfkbia","Nfkbiz","F2rl1","Areg","Defb1","Sftpd","Sftpb","Sftpa1","Muc1","Muc4","Muc16","Muc20","Muc5b","Muc5ac","Muc2","Lyz2","Ltf","Slpi","Lcn2","Pigr","Chil4","Itln1","Lbp","Lgals3","Reg3g")),group.by = "ident", x.lab.rot = T,plot.legend = T,do.return = T)+rotate()+ theme(axis.text.x = element_text(angle = 45, vjust = 1,hjust=1)) #this scales both genotypes together
```

##### Mucosal chemokines (ccl25, ccl28, cxcl14, and cxcl17):
```{r, fig.height=3, fig.width=8}
library(ggpubr)

ggplot(df_all_wt[(df_all_wt$specific_type %in% c("Basal","Transitional","Ciliated","Secretory","CiliaSecretory","CyclingEpithelial")),],aes(age,Cxcl17))+facet_grid(.~specific_type)+geom_dotplot(binaxis="y",aes(fill=age),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.2)+ stat_summary(aes(color=age),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))+ theme(axis.text.x = element_text(angle = 45,hjust=1))
```


##### To look into genes correlated with scGPS scores:
```{r}
Ciliopathy_cor_test<-apply(wt_ciliopathy_table, 2, function(x) cor.test(seurat_E15E16P1P4_wt@meta.data$ciliopathy_score,x,method="pearson")) 
```
```{r}
df_Ciliopathy_cor_test<-as.data.frame(do.call(rbind, Ciliopathy_cor_test))

```
```{r}
df_order_Ciliopathy_cor_test<-df_Ciliopathy_cor_test[order(unlist(df_Ciliopathy_cor_test$estimate),decreasing = TRUE),]
```
```{r}
tidy_Ciliopathy_cor<-cbind(df_order_Ciliopathy_cor_test$estimate,df_order_Ciliopathy_cor_test$p.value)
```

```{r}
colnames(tidy_Ciliopathy_cor)<-c("cor","p.value")
```

```{r}
tidy_Ciliopathy_cor
```
```{r}
unlist(tidy_Ciliopathy_cor[,1])

```

```{r}

#volc_Ciliopathy_cor<-mutate(tidy_Ciliopathy_cor,sig=ifelse(tidy_Ciliopathy_cor$p.value<0.01,"P_adj<0.01","Not Sig"))
df_tidy_ciliopathy_cor<-data.frame(matrix(unlist(tidy_Ciliopathy_cor), nrow=112, byrow=F),stringsAsFactors=FALSE)
colnames(df_tidy_ciliopathy_cor)<-c("cor","p.value")
df_tidy_ciliopathy_cor$gene<-rownames(tidy_Ciliopathy_cor)
```
```{r}
library(ggrepel)
ggplot(df_tidy_ciliopathy_cor, aes(cor, p.value))+geom_point() +geom_text_repel(data=filter(df_tidy_ciliopathy_cor,cor>0.45), aes(label=gene))+ylim(-2e-05,2.5e-05)
p
p+geom_text_repel(data=filter(as.data.frame(tidy_Ciliopathy_cor), p_val_adj<0.05), aes(label=rownames(tidy_Ciliopathy_cor)))
```
```{r}
wt_ciliopathy_cluster<-data.frame(wt_ciliopathy_table,seurat_E15E16P1P4_wt@meta.data$res.1.4)
aggre_wt_ciliopathy_cluster<-aggregate(wt_ciliopathy_cluster[, 1:112], list(wt_ciliopathy_cluster[,113]), median)
#aggre_wt_ciliopathy_cluster[aggre_wt_ciliopathy_cluster$Group.1==8,][order(aggre_wt_ciliopathy_cluster[aggre_wt_ciliopathy_cluster$Group.1==8,],decreasing = TRUE)]
```
```{r}
x<-sort(aggre_wt_ciliopathy_cluster[aggre_wt_ciliopathy_cluster$Group.1==8,],decreasing = TRUE)
unlist(x)[2:30]
```
```{r}
wt_mucosa_cluster<-data.frame(percentile_table_E15E16P1P4wt[,colnames(percentile_table_E15E16P1P4wt) %in% geneList$Mucosa.epithelium.goblet],seurat_E15E16P1P4_wt@meta.data$res.1.4)
aggre_wt_mucosa_cluster<-aggregate(wt_mucosa_cluster[, 1:301], list(wt_mucosa_cluster[,302]), median)

```
```{r}
unlist(sort(aggre_wt_mucosa_cluster[aggre_wt_mucosa_cluster$Group.1==8,],decreasing = TRUE))[2:51]
```











